Auto Activation for SaaS Private Offers is a Game Changer!
Shout out to Microsoft Marketplace for launching AUTO ACTIVATION for SaaS private offers. This has simplified our sales operations for Microsoft Marketplace private offers, as well as dramatically improved our seller and customer experience with by eliminating the #1 reason we've historically seen block customers from completing Marketplace SaaS private offer procurement. Auto activation for private offers allows customers to skip the step of going to our SaaS landing page and activate Marketplace SaaS billing by having to SSO with their AAD. The problem with this step is that most of our customers are in regulated industries, and their AAD admins do not allow SSO on SaaS landing pages without enabling profile sharing, which requires a Global Admin to enable. We've encountered far too many times where our customer is scrambling to find their Global Admin so that they can complete the final steps to finish the steps to activate Marketplace SaaS billing. This also puts our sellers in the awkward position of having to help customers diagnose and troubleshoot the situation, which as a rule they are not equipped to, or find a Microsoft resource to help. Now, with this new feature the whole SSO to our SaaS landing page and associated problems are eliminated. Ever since we started using it our customers' procurement experience has been faster and smoother. I'm looking forward to more improvements to the Marketplace experience! Here is the feature: https://learn.microsoft.com/en-us/partner-center/marketplace-offers/create-new-saas-offer-plans#configure-auto-activationDesign observability for AI apps and agents selling through Microsoft Marketplace
In the last post, API resilience and reliability patterns for AI apps and agents, we focused on what happens when AI systems encounter failure—and how resilient execution paths keep that failure contained. Timeouts fire with intent. Retries stay bounded. Circuit breakers provide overload protection. When resilience is designed well, your system continues to function even as conditions change, forming the foundation of AI reliability engineering. You can always get curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor. This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace. Observability for AI systems AI apps and agents are shifting traditional observability, which was designed for systems based on simple assumptions, where requests followed linear paths and workloads behaved predictably. Execution in AI systems consumes tokens at a highly variable rate rather than fixed compute units. Requests unfold across multiple reasoning steps. Agents perform work that spans APIs, models, retrieval layers, and applications. A single interaction may pause, branch, retry, or exit early depending on inferred intent, context, and constraints. Instead of asking whether services are running, observability for AI systems asks: what is the system doing right now—and why? Is an agent spending its time reasoning, waiting on dependencies, retrying tool calls, or exiting early due to enforced limits? Is cost increasing because value is increasing, or because execution paths are expanding without progress? AI observability requirements shift the focus in the following subtle, but critical ways: From resource availability to workflow state From performance metrics to signals From incidents to patterns Core observability dimensions for AI apps and agents Once observability shifts toward understanding behavior, clarity comes from tracking state across the agents in the workflow. For AI apps and agents, observable indicators, such as those detailed below, show how work unfolds and changes during real usage—especially in trials and early adoption: Execution flow shows how a request moves through agents, tools, and workflows. This highlights where execution progresses smoothly, where it slows, and where it concludes early. This makes agent outcomes explainable and keeps behavior consistent across tenants. Cost and token behavior reveals how execution translates into consumption. Token usage per request, per agent step, and per retry shows where value is being delivered and where execution paths expand without proportional benefit. This insight connects runtime behavior directly to Marketplace billing expectations and evaluations. Latency and wait states distinguish active processing from time spent waiting on dependencies. Seeing where time is consumed helps explain slow experiences and guides decisions about optimization, caching, or resilience improvements. Failure classification provides structure when systems degrade and supports effective AI incident management. Separating tool failures from planning failures, and transient issues from terminal exits, keeps investigations focused and prevents protective behavior from being misread as instability. Tenant‑level patterns surface how behavior repeats at scale. Uneven load, and recurring degradation often appear first during trials and shape the customer's perception. Together, these dimensions turn telemetry into understanding—supporting clearer conversations, faster triage, and predictable execution as usage grows. Why observability matters By this point in the journey, your AI app or agent has implemented bounded execution paths, cost controls, and quality of service safeguards. As a result, failure degrades gracefully instead of spreading. These resilience techniques determine how your solution behaves under pressure. The data gathered from observability platforms like Application Insights and Azure Monitor explains why it behaves that way. For AI and agentic systems, infrastructure health alone rarely answers the questions that matter. Services can be up, CPUs can be idle, and queues can look healthy while agents loop inefficiently, retries quietly expand cost, or workflows exit early without delivering value. From the customer’s perspective, the experience feels inconsistent even though the platform appears stable. AI app observability closes this gap by revealing system behavior rather than system status. It shows how requests move, where work concentrates, and how constraints shape outcomes. At Marketplace scale, these patterns repeat across tenants and trials. What appears once during an evaluation often appears again as adoption grows. Observability connects runtime behavior back to the design choices introduced in earlier posts: Usage‑based billing introduced variability in consumption Performance optimization introduced tradeoffs among latency, quality, and cost Resilience patterns introduced controlled failure and bounded execution Observability allows you to explain outcomes during trials, validate assumptions as usage grows, and support post-launch AI operations confidence across customers and environments. Without this visibility, teams react to symptoms. With it, they recognize patterns. From execution paths to behavioral signals Observability begins at the same place resilience begins—API boundaries. These boundaries define where responsibility shifts and where behavior becomes visible. Observability focuses on signals that explain decisions made by the system as it executes instead of relying on raw logs that describe isolated events. Every resilience mechanism emits behavioral signals. Viewed together, these signals provide far more value than logs alone. Logs answer whether something happened. Behavioral signals explain why it happened and how the system responded. Circuit breakers change state as load builds and recedes. Retry loops show whether failures resolve quickly or exhaust their limits. Timeout enforcement reveals where dependencies slow execution. Fallback paths and early terminations show how the system protects itself while preserving outcomes for customers. This perspective matters most for agents. Agent execution unfolds as a series of choices—plan, call a tool, retry, exit early—rather than a single request‑response cycle, which requires monitoring AI agent behavior to remain understandable and consistent at scale. Observability that tracks these decisions makes agent behavior understandable, consistent, and defensible as usage grows across customer tenants. Observability at the agent layer As AI systems become more agent‑driven, observability needs to move closer to where decisions are made. Agents introduce variability by design. They plan, adapt, and choose workflow paths dynamically. Without first‑class visibility into that behavior, execution can appear unpredictable even when the underlying system is healthy. Observability at the agent layer acts as the feedback loop that keeps execution safely bounded. It shows how agents use the freedom you give them—and where that freedom begins to stretch into inefficiency. Observability follows how the agent did its job instead of treating the agent’s interaction as a single outcome. Several indicators help make agent behavior understandable. Step count per request reveals how much reasoning effort a prompt requires. Planning iterations show whether an agent converges quickly or cycles through alternatives. Tool invocation frequency highlights when agents rely heavily on external systems. Early exits compared to full completion explain whether limits and fallbacks activate as designed. Taken together, these indicators help distinguish healthy exploration from inefficient reasoning and degraded execution. An agent exploring briefly before converging adds value. An agent looping through tools without progress signals pressure, uncertainty, or dependency issues. This distinction reinforces a core principle of agentic systems: models reason probabilistically, adapting to context as it changes. Your system observes deterministically—measuring execution, enforcing boundaries, and clarifying outcomes. When those roles stay separate and well‑instrumented, agent behavior becomes transparent, predictable, and ready for Marketplace scale. Observability across environments The type of Marketplace offer you choose shapes what observability customers expect and how responsibility is shared. For SaaS offers, publishers typically own end‑to‑end execution. Observability centers on agent behavior, workflow completion, token usage, latency, and dependency impact across tenants. Publishers rely on consistent signals—often surfaced through tools like Azure Monitor, Application Insights, and Microsoft AI Foundry—to explain how requests behave as scale and load increase. For container‑based offers and Azure Managed Applications, observability expectations are more distributed. Publishers expose clear execution outcomes, limits, and failure signals at application boundaries. Customers, in turn, observe infrastructure health, scaling behavior, and downstream systems within their own environments. This separation ensures each party has visibility into what they control without creating ambiguity. Learn more about Choosing your marketplace offer type for AI Apps and agents. Execution behavior differs across environments for predictable reasons. Scale increases, tenant mix broadens, and external dependencies behave differently under real load. What must stay consistent is how behavior is interpreted. Signal definitions, thresholds, and failure classification should mean the same thing in Dev, Stage, and Prod. Learn more about designing a reliable environment strategy for Microsoft Marketplace AI apps and agents. Staging environments are where this consistency is validated. Observing retries, timeouts, and graceful degradation before production prepares you for Marketplace evaluations, which often resemble production conditions. Observability gaps tend to appear first during customer evaluation—when clarity matters most. Publisher and customer visibility boundaries Purpose: Parallel Post #13 responsibility clarity, now for observability As observability matures across environments, clarity around responsibility becomes essential. For Marketplace solutions, trust grows when publishers and customers each see what they own—and understand where that visibility ends. Publishers are responsible for instrumenting execution paths end to end. That means making workflows traceable, limits visible, and failure modes explainable. Observability should surface behavior—how requests progressed, where execution concluded, and why—rather than exposing raw internal errors that require insider knowledge to interpret. Customers focus their observability on what they control. This includes monitoring downstream systems, infrastructure behavior, and environment‑level alerts within their own estate. When visibility aligns with ownership, teams can act quickly and decisively. Exposing too much internal detail can overwhelm customers and blur accountability. Observing too little behavior creates friction, especially when issues cross boundaries and lack context. Clear visibility enables faster triage, sharper ownership boundaries, and fewer escalations rooted in ambiguity. Observability as an enabler for scale, billing, and trust From a customer’s perspective, observability answers two fundamental questions: Can I understand what happened? and Can I trust this at scale? When the answer to both is clear, observability becomes part of the value your Marketplace offering delivers. When system behavior is visible and explainable, customers gain confidence that adoption and growth will remain predictable. Observability directly supports usage‑based billing by tying execution behavior to measured consumption. Clear visibility into token usage, retries, and execution paths helps validate how usage is calculated and supports transparent billing conversations. It also enables ongoing performance tuning and caching strategies by showing where latency accumulates, where work repeats, and where optimization delivers measurable impact. Observability reinforces confidence in resilience mechanisms, confirming that limits, fallbacks, and degradation paths activate as designed under real‑world conditions. Beyond validation, observability creates a continuous feedback loop. Execution data informs pricing adjustments, guides changes to limits, and helps refine default configurations as customer behavior evolves. What’s next in the journey With execution behavior observable and explainable, the focus shifts to how AI systems are operated safely as change accelerates. The upcoming posts will discuss deployment strategies, CI/CD pipelines for agents, and progressive rollouts build on this foundation—ensuring AI apps evolve confidently as usage and expectations grow. Key Resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor Quick-Start Development Toolkit can connect you with code templates for AI solution patterns Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV SuccessDesigning a reliable environment strategy for Microsoft Marketplace AI apps and agents
Technical guidance for software companies Delivering an AI app or agent through Microsoft Marketplace requires more than strong model performance or a well‑designed user flow. Once your solution is published, both you and your customers must be able to update, test, validate, and promote changes without compromising production stability. A structured environment strategy—Dev, Stage, and Production—is the architectural mechanism that makes this possible. This post provides a technical blueprint for how software companies and Microsoft Marketplace customers should design, operate, and maintain environment separation for AI apps and agents. It focuses on safe iteration, version control, quality gates, reproducible deployments, and the shared responsibility model that spans publisher and customer tenants. You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor. This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace. Why environment strategy is a core architectural requirement Environment separation is not just a DevOps workflow. It is an architectural control that ensures your AI system evolves safely, predictably, and traceably across its lifecycle. This is particularly important for Marketplace solutions because your changes impact not just your own environment, but every tenant where the solution runs. AI‑driven systems behave differently from traditional software: Prompts evolve and drift through iterative improvements. Model versions shift, sometimes silently, affecting output behavior. Tools and external dependencies introduce new boundary conditions. Retrieval sources change over time, producing different Retrieval Augmented Generation (RAG) contexts. Agent reasoning is probabilistic and can vary across environments. Without explicit boundaries, an update that behaves as expected in Dev may regress in Stage or introduce unpredictable behavior in Production. Marketplace elevates these risks because customers rely on your solution to operate within enterprise constraints and support AI scalability for enterprise. A well‑designed environment strategy answers the fundamental operational question: How does this solution change safely over time? Publisher-managed environment (tenant) Software companies publishing to Marketplace must maintain a clear three‑tier environment strategy. Each environment serves a distinct purpose and enforces different controls. Development environment: Iterate freely, without customer impact In Dev, engineers modify prompts, adjust orchestration logic, integrate new tools, and test updated model versions. This environment must support: Rapid prompt iteration with strict versioning, never editing in place. Model pinning, ensuring inference uses a declared version. Isolated test data, preventing contamination of production RAG contexts. Feature‑flag‑driven experimentation, enabling controlled testing. Staging environment: Validate behavior before promotion Stage is where quality gates activate. All changes—including prompt updates, model upgrades, new tools, and logic changes—must pass structured validation before they can be promoted. This environment enforces: Integration testing that supports AI app performance optimization Acceptance criteria Consistency and performance baselines Safety evaluation and limits enforcement Production environment: Serve customers with reliability and rollback readiness Solutions running in production environments, regardless of whether they are publisher hosted or deployed into a customer's tenant must provide: Stable, predictable behavior, supported by deliberate AI workload capacity planning Strict separation from test data sources Clearly defined rollback paths Auditability for all environment‑specific configurations This model highlights the core environments required for Marketplace readiness; in practice, publishers may introduce additional environments such as integration, testing, or preproduction depending on their delivery pipeline. The customer tenant deployment model: Deploying safely across customer environments Once a Marketplace customer purchases and deploys your AI app or agent, they must be able to deploy and maintain your solution across all their environments without reverse engineering your architecture. A strong offer must provide: Repeatable deployments across all heterogeneous environments. Predictable configuration separation, including identity, data sources, and policy boundaries. Customer‑controlled promotion workflows—updates should never be forced. No required re‑creation of environments for each new version. Publishers should design deployment artifacts such that customers do not have to manually re‑establish trust boundaries, identity settings, or configuration details each time the publisher releases a solution update. Plan for AI‑specific environment challenges AI systems introduce behavioral variances that traditional microservices do not. Your environment strategy must explicitly account for them. Prompt drift Prompts that behave well in one environment may respond differently in another due to: Different user inputs, where production prompts encounter broader and less predictable queries than test environments Variation in RAG contexts, driven by differences in indexed content, freshness, and data access Model behavior shifts under scale, including concurrency effects and token pressure, which also affects cost and requires attention to cost optimization for AI apps Tool availability differences, where agents may have access to different tools or permissions across environments This requires explicit prompt versioning and environment-based promotion. Model version mismatches If one environment uses a different model version or even a different checkpoint, behavior divergence will appear immediately. Publishers should account for the following model management best practices: Model version pinning per environment Clear promotion paths for model updates RAG context variation Different environments may retrieve different documents unless seeded on purpose. Publishers should ensure their solutions avoid: Test data appearing in production environments Production data leaking into non-production environments Cross contamination of customer data in multi-tenant SaaS solutions Make sure your solution accounts for stale-data and real-time data. Agent variability Agents exhibit stochastic reasoning paths, which becomes more pronounced when scaling AI agents. Environments must enforce: Controlled tool access Reasoning step boundaries Consistent evaluation against expected patterns Publisher–customer boundary: Shared responsibilities Marketplace AI solutions span publisher and customer tenants, which means environment strategy is jointly owned. Each side has well-defined responsibilities. Publisher responsibilities Publishers should: Design an environment model that is reproducible inside customer tenants. Provide clear documentation for environment-specific configuration. Ensure updates are promotable, not disruptive, by default. Capture environment‑specific logs, traces, and evaluation signals to support debugging, audits, and incident response. Customer responsibilities Customers should: Maintain environment separation using their governance practices. Validate updates in staging before deploying them in production. Treat environment strategy as part of their operational contract with the publisher. Environment strategies support Marketplace readiness A well‑defined environment model is a Marketplace accelerator. It improves: Onboarding Customers adopt faster when: Deployments are predictable Configurations are well scoped Updates have controlled impact Long-term operations Strong environment strategy reduces: Regression risk Customer support escalations Operational instability Solutions that support clear environment promotion paths have higher retention and fewer incidents. What’s next in the journey The next architectural decision after environment separation is identity flow across these environments and across tenant boundaries, especially for AI agents acting on behalf of users. The follow‑up post will explore tenant linking, OAuth consent patterns, and identity‑plane boundaries in Marketplace AI architectures. See the next post in the series: Designing Tenant Linking to Scale Microsoft Marketplace AI Apps. Key Resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor Quick-Start Development Toolkit can connect you with code templates for AI solution patterns Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV SuccessQuality and evaluation framework for successful AI apps and agents in Microsoft Marketplace
Why quality in AI is different — and why it matters for Marketplace Traditional software quality spans many dimensions — from performance and reliability to correctness and fault tolerance — but once those characteristics are specified and validated, system behavior is generally stable and repeatable. Quality is assessed through correctness, reliability, performance, and adherence to specifications. AI apps and agents change this equation. Their behavior is inherently non-deterministic and context‑dependent. The same prompt can produce different responses depending on model version, retrieval context, prior interactions, or environmental conditions, which is why non-deterministic AI testing is required to evaluate it reliably. For agentic systems, quality also depends on reasoning paths, tool selection, and how decisions unfold across multiple steps — not just on the final output. This means an AI app can appear functional while still falling short on quality: producing responses that are inconsistent, misleading, misaligned with intent, or unsafe in edge cases. Without a structured evaluation framework, these gaps often surface only in production — in customer environments, after trust has already been extended. For Microsoft Marketplace, this distinction matters. Buyers expect AI apps and agents to behave predictably, operate within clear boundaries, and remain fit for purpose as they scale. AI quality evaluation turns those expectations into something observable — and that visibility is what determines Marketplace readiness. You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor. This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace. How quality measurement shapes Marketplace readiness AI apps and agents that can demonstrate quality — with documented evaluation frameworks, defined release criteria, and evidence of ongoing measurement — are easier to evaluate, trust, and adopt. Quality evidence reduces friction during Marketplace review, clarifies expectations during customer onboarding, and supports long-term confidence in production. When quality is visible and traceable, the conversation shifts from "does this work?" to "how do we scale it?" — which is exactly where publishers want to be. Publishers who treat quality as a first-class discipline build the foundation for safe iteration, customer retention, and sustainable growth through Microsoft Marketplace. That foundation is built through the decisions, frameworks, and evaluation practices established long before a solution reaches review. What "quality" means for AI apps and agents Quality for AI apps and agents is not a single metric — it spans interconnected dimensions that together define whether a system is doing what it was built to do, for the people it was built to serve. The HAX Design Library — Microsoft's collection of human-AI interaction design patterns — offers practical guidance for each one. These dimensions must be defined before evaluation begins. You can only measure what you have first described. Accuracy and relevance — does the output reflect the right answer, grounded in the right context? HAX patterns Make clear what the system can do (G1) and notify users when the AI is uncertain (G10) help publishers design systems where accuracy is visible and outputs are understood in the right context — not treated as universally authoritative. Safety and alignment — does the output stay within intended use, without harmful, biased, or policy-violating content? HAX patterns Mitigate social biases (G6) and Support efficient correction (G9) help ensure outputs stay within acceptable boundaries — and that users can identify and address issues before they cause downstream harm. Consistency and reliability — does the system behave predictably across users, sessions, and environments? HAX patterns Remember recent interactions (G12) and notify users about changes (G18) keep behavior coherent within sessions and ensure updates to the model or prompts are never silently introduced. Fitness for purpose — does the system do what it was designed to do, for the people it was designed to serve, in the conditions it will actually operate in? HAX patterns make clear how well the system can do what it does (G2) and Act on the user's context and goals (G4) ensure the system responds to what users actually need — not just what they literally typed. These dimensions work together — and gaps in any one of them will surface in production, often in ways that are difficult to trace without a deliberate evaluation framework. Designing an evaluation framework before you ship Evaluation frameworks should be built alongside the solution. At the end, gaps are harder and costlier to close. The discipline mirrors the design-in approach that applies to security and governance: decisions made early shape what is measurable, what is improvable, and what is ready to ship. A well-structured evaluation framework defines five things: What to measure — the quality dimensions that matter most for this solution and its intended use cases. For AI apps and agents, this typically includes task adherence, response coherence, groundedness, and safety — alongside the fitness-for-purpose dimensions defined in the previous section. How to measure it — the methods, tools, and benchmarks used to assess quality consistently. Effective evaluation combines AI-assisted evaluators (which use a model as a judge to score outputs), rule-based evaluators (which apply deterministic logic), and human review for edge cases and safety-relevant responses that automated methods cannot fully capture. Who evaluates — the right combination of automated metrics, human review, and structured customer feedback. No single method is sufficient; the framework defines how each is applied and when human judgment takes precedence. When to evaluate — at defined milestones: during development to establish a baseline, pre-release to validate against acceptance thresholds, at rollout to catch regression, and continuously in production to detect drift as models, prompts, and data evolve. What triggers re-evaluation — model updates, prompt changes, new data sources, tool additions, or meaningful shifts in customer usage patterns. Re-evaluation should be a scheduled and triggered discipline, not an ad hoc response to visible failures. The framework becomes a shared artifact — used by the publisher to release safely, and by customers to understand what quality commitments they are adopting when they deploy the solution in their environment. Evaluate your AI agents - Microsoft Foundry | Microsoft Learn Evaluation methods for AI apps and agents Quality must be assessed across complementary AI evaluation methods — each designed to surface a different category of risk, at a different stage of the solution lifecycle. Automated metric evaluation — evaluators assess agent responses against defined criteria at scale. Some use AI models as judges to score outputs like task adherence, coherence, and groundedness; others apply deterministic rules or text similarity algorithms. Automated evaluation is most effective when acceptance thresholds are defined upfront — for example, a minimum task adherence pass rate before a release proceeds. Safety evaluation — a dedicated evaluation category that identifies potential content risks, policy violations, and harmful outputs in generated responses. Safety evaluators should run alongside quality evaluators, not as a separate afterthought. Human-in-the-loop evaluation — structured expert review of edge cases, borderline outputs, and safety-relevant responses that automated metrics cannot fully capture. Human judgment remains essential for interpreting context, intent, and impact. AI agent red-teaming and adversarial testing — probing the system with challenging, unexpected, or intentionally misused inputs (including prompt injection attempts and tool misuse) to surface failure modes before customers encounter them. Microsoft provides dedicated AI red teaming guidance for agent-based systems. Customer feedback loops — structured collection of real-world signals from users interacting with the system in production. Production feedback closes the gap between what was tested and what customers actually experience. Each method has a distinct role. The evaluation framework defines when and how each is applied — and which results are required before a release proceeds, a change is accepted, or a capability is expanded. Defining release criteria and ongoing quality gates Quality evaluation only drives improvement when it is connected to clear release criteria, often enforced through an LLMOps quality gate. In an LLMOps model, those criteria are automated gates embedded directly into the CI/CD pipeline, applied consistently at every stage of the release cycle. In continuous integration (CI), automated evaluations run with every change — whether that change is a prompt update, a model version, a new tool, or a data source modification. CI gates catch regressions early, before they reach customers, by validating outputs against predefined quality thresholds for task adherence, coherence, groundedness, and safety. In continuous deployment (CD), quality gates determine whether a build is eligible to proceed. Release criteria should define: Minimum acceptable thresholds for each quality dimension — a release does not proceed until those thresholds are met Known failure modes that block release outright versus those that are tracked, monitored, and accepted within defined risk tolerances Deployment constraints — conditions under which a release is paused, rolled back, or progressively expanded to a subset of users before full rollout Ongoing evaluation must be scheduled and triggered. As models, prompts, tools, and customer usage patterns evolve, the baseline shifts. LLMOps treats re-evaluation as a continuous discipline: run evaluations, identify weak areas, adjust, and re-evaluate before changes propagate. This connects directly to governance. Quality evidence — the record of what was measured, when, and against what criteria — is part of the audit trail that makes AI behavior accountable, explainable, and trustworthy over time. For more on the governance foundation this builds on, see Governing AI apps and agents for Marketplace readiness. Quality across the publisher-customer boundary Clear quality ownership reduces friction at onboarding, builds confidence during operation, and protects both parties when behavior deviates. In the Marketplace context, quality is a shared responsibility — but the boundaries are distinct. Publishers are responsible for: Designing and running the evaluation framework during development and release Defining quality dimensions and thresholds that reflect the solution's intended use Providing customers with transparency into what quality means for this solution — without exposing proprietary prompts or internal logic Customers are responsible for: Validating that the solution performs appropriately in their specific environment, with their data and their users Configuring feedback and monitoring mechanisms that surface quality signals in their tenant Treating quality evaluation as a shared ongoing responsibility, not a one-time publisher guarantee When both sides understand their role, quality stops being a handoff and becomes a foundation — one that supports adoption, sustains trust, and enables both parties to respond confidently when behavior shifts. What's next in the journey A strong quality framework sets the baseline — but keeping that quality visible as solutions scale is its own discipline. The next posts in this series explore what comes after the framework is in place: API resilience, performance optimization, and operational observability for AI apps and agents running in production environments. See the next post in the series: Designing a reliable environment strategy for Microsoft Marketplace AI apps and agents | Microsoft Community Hub. Key resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor Quick-Start Development Toolkit can connect you with code templates for AI solution patterns Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV SuccessGoverning AI apps and agents for Marketplace
Governing AI apps and agents Governance is what turns powerful AI functionality into a solution that enterprises can confidently adopt, operate, and scale—an essential part of AI governance for agents. It establishes clear responsibility for actions taken by the system, defines explicit boundaries for acceptable behavior, and creates mechanisms to review, explain, and correct outcomes over time. Without this structure, AI systems can become difficult to manage as they grow more connected and autonomous. For publishers, governance is how trust is earned—and sustained—in enterprise environments, enabling responsible AI operations. It signals that AI behavior is intentional, accountable, and aligned with customer expectations, not left to inference or assumption. As AI apps and agents operate across users, data, and systems, risk shifts away from what a model can generate and toward how its behavior is governed in real‑world conditions. Marketplace readiness reflects this shift, defined less by raw capability and more by control, accountability, trust, and adherence to AI compliance standards for publishing. You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor. This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace. What governance means for AI apps and agents Governance in AI systems is operational and continuous. It is not limited to documentation, checklists, or periodic reviews — it shapes how an AI app or agent behaves while it is running in real customer environments. For AI apps and agents, governance spans three closely connected dimensions: Policy What the system is allowed to do, what data it is allowed to access, what is restricted, and what is explicitly prohibited. Enforcement How those policies are applied consistently in production, even as context, inputs, and conditions change. Evidence How decisions and actions are traced, reviewed, and audited over time. Governance works when intent, behavior, and proof move together — turning expectations into outcomes that can be trusted and examined. These dimensions are interdependent. Policy without enforcement is aspiration. Enforcement without evidence is unverifiable. Governance in action Governance becomes real when responsibility is explicit. For AI apps and agents, this starts with clarity around who is responsible for what: Who the agent acts for — and how its use protects business value Ensuring the agent is used for its intended purpose, produces measurable value, and is not misused, over‑extended, or operating outside approved business contexts. Who owns data access and data quality decisions Governing how the agent consumes and produces data, whether access is appropriate, and whether the data used or generated is reliable, accurate, and aligned with business and integrity expectations. Who is accountable for outcomes when behavior deviates Defining responsibility when the agent’s behavior creates risk, degrades value, or produces unexpected outcomes — so corrective action is timely, intentional, and owned. When governance is left vague or undefined, accountability gaps surface and agent actions become difficult to justify and explain across the publisher, the customer, and the solution itself. In this model, responsibility is shared but distinct. The publisher is responsible for designing and implementing the governance capabilities within the solution — defining boundaries, enforcement points, and evidence mechanisms that protect business value by default. Marketplace customers expect to understand who is accountable before they adopt an AI solution, not after an incident forces the question. The customer is responsible for configuring, operating, and applying those capabilities within their own environment, aligning them to internal policies, risk tolerance, and day‑to‑day use. Governance works when both roles are clear: the publisher provides the structure, and the customer brings it to life in practice. Data governance for AI: beyond storage and access For Marketplace‑ready AI apps and agents, data governance must account for where data moves, not just where it resides. Understanding how data flows across systems, tools, and tenants is essential to maintaining trust as solutions scale. Data governance for AI apps and agents extends beyond where data is stored. These systems introduce new artifacts that influence behavior and outcomes, including prompts and responses, retrieval context and embeddings, and agent‑initiated actions and tool outputs. Each of these elements can carry sensitive information and shape downstream decisions. Effective data governance for AI apps and agents requires clear structure: Explicit data ownership — defining who owns the data and under what conditions it can be accessed or used Access boundaries and context‑aware authorization — ensuring access decisions reflect identity, intent, and environment, not just static permissions Retention, auditability, and deletion strategies — so data use remains traceable and aligned with customer expectations over time Relying on prompts or inferred intent to determine access is a governance gap, not a shortcut. Without explicit controls, data exposure becomes difficult to predict or explain. Runtime policy enforcement in production Policies are stress tested when the agent is responding to real prompts, touching real data, and taking actions that carry real consequences. For software companies building AI apps and agents for Microsoft Marketplace, runtime policy enforcement is also how you keep the system fit for purpose: aligned to its intended use, supported by evidence, and constrained when conditions change. At runtime, governance becomes enforceable through three clear lanes of behavior: Decisions that require human approval Use approval gates for higher‑impact steps (for example: executing a write operation, sending an external request, or performing an irreversible workflow). This protects the business value of the agent by preventing “helpful” behavior from turning into misuse. Actions that can proceed automatically — within defined limits Automation is earned through clarity: define the agent’s intended uses and keep tool access, data access, and action scope anchored to those uses. Fit‑for‑purpose isn’t a feeling — it’s something you support with defined performance metrics, known error types, and release criteria that you measure and re‑measure as the system runs. Behaviors that are never permitted — regardless of context or intent Block classes of behavior that violate policy (including jailbreak attempts that try to override instructions, expand tool scope, or access disallowed data). When an intended use is not supported by evidence — or new evidence shows it no longer holds — treat that as a governance trigger: remove or revise the intended use in customer‑facing materials, notify customers as appropriate, and close the gap or discontinue the capability. To keep runtime enforcement meaningful over time, pair it with ongoing evaluation: document how you’ll measure performance and error patterns, run those evaluations pre‑release and continuously, and decide how often re‑evaluation is needed as models, prompts, tools, and data shift. This is what keeps autonomy intentional. It allows AI apps and agents to operate usefully and confidently, while ensuring behavior remains aligned with defined expectations — and backed by evidence — as systems evolve and scale. Auditability, explainability, and evidence Guardrails are the points in the system where governance becomes observable: where decisions are evaluated, actions are constrained, and outcomes are recorded. As described in Designing AI guardrails for apps and agents in Marketplace, guardrails shape how AI systems reason, access data, and take action — consistently and by default. Guardrails may be embedded within the agent itself or implemented as a separate supervisory layer — another agent or policy service — that evaluates actions before they proceed. Guardrail responses exist on a spectrum. Some enforce in the moment — blocking an action or requiring approval before it proceeds — while others generate evidence for post‑hoc review, supported by audit logging for AI agents. Marketplace‑ready AI apps and agents could implement both, with the response mode matched to the severity, reversibility, and business impact of the action in question. These expectations align with the governance and evidence requirements outlined in the Microsoft Responsible AI Standard v2 General Requirements. In practice, guardrails support auditability and explainability by: Constraining behavior at design time Establishing clear defaults around what the system can and cannot do, so intended use is enforced before the system ever reaches production. Evaluating actions at runtime Making decisions visible as they happen — which tools were invoked, which data was accessed, and why an action was allowed to proceed or blocked. When governance is unclear, even strong guardrails lose their effectiveness. Controls may exist, but without clear intent they become difficult to justify, unevenly applied across environments, or disconnected from customer expectations. Over time, teams lose confidence not because the system failed, but because they can’t clearly explain why it behaved the way it did. When governance and guardrails are aligned, the result is different. Behavior is intentional. Decisions are traceable. Outcomes can be explained without guesswork. Auditability stops being a reporting exercise and becomes a natural byproduct of how the system operates day to day. Aligning governance with Marketplace expectations Governance for AI apps and agents must operate continuously, across all in‑scope environments — in both the publisher’s and the customer’s tenants. Marketplace solutions don’t live in a single boundary, and governance cannot stop at deployment or certification. Runtime enforcement is what keeps governance active as systems run and evolve. In practice, this means: Blocking or constraining actions that violate policy — such as stopping jailbreak attempts that try to override system instructions, escalate tool access, or bypass safety constraints through crafted prompts Adapting controls based on identity, environment, and risk — applying stricter limits when an agent acts across tenants, accesses sensitive data, or operates with elevated permissions Aligning agent behavior with enterprise expectations in real time — ensuring actions taken on behalf of users remain within approved roles, scopes, and approval paths These controls matter because AI behavior is dynamic. The same agent may behave differently depending on context, inputs, and downstream integrations. Governance must be able to respond to those shifts as they happen. Runtime enforcement is distinct from monitoring. Enforcement determines what is allowed to continue. Monitoring explains what happened once it’s already done. Marketplace‑ready AI solutions need both, but governance depends on enforcement to keep behavior aligned while it matters most. Operational health through auditability and traceability Operational health is the combination of traceability (what happened) and intelligibility (how to use it responsibly). When both are present, governance becomes a quality signal customers can feel day to day — not because you promised it, but because the system consistently behaves in ways they can understand and trust. Healthy AI apps and agents are not only traceable — they are intelligible in the moments that matter. For Marketplace customers, operational trust comes from being able to understand what the system is intended to do, interpret its behavior well enough to make decisions, and avoid over‑relying on outputs simply because they are produced confidently. A practical way to ground this is to be explicit about who needs to understand the system: Decision makers — the people using agent outputs to choose an action or approve a step Impacted users — the people or teams affected by decisions informed by the system’s outputs Once those stakeholders are clear, governance shows up as three operational promises you can actually support: Clarity of intended use Customers can see what the agent is designed to do (and what it is not designed to do), so outputs are used in the right contexts. Interpretability of behavior When an agent produces an output or recommendation, stakeholders can interpret it effectively — not perfectly, but reasonably well — with the context they need to make informed decisions. Protection against automation bias Your UX, guidance, and operational cues help customers stay aware of the natural tendency to over‑trust AI output, especially in high‑tempo workflows. This is where auditability and traceability become more than logs. Well governed AI systems should still answer: Who initiated an action — a user, an agent acting on their behalf, or an automated workflow What data was accessed — under which identity, scope, and context What decision was made, and why — especially when downstream systems or people are affected The logs should show evidence that stakeholders can interpret those outputs in realistic conditions — and there is a method to evaluate this, with clear criteria for release and ongoing evaluation as the solution evolves. Explainability still needs balance. Customers deserve transparency into intended use, behavior boundaries, and how to interpret outcomes — without requiring you to expose proprietary prompts, internal logic, or implementation details. For more information on securing your AI apps and agents, visit Securing AI apps and agents on Microsoft Marketplace | Microsoft Community Hub. What's next in the journey Governance creates the conditions for AI apps and agents to operate with confidence over time. With clear policies, enforcement, and evidence in place, publishers are better prepared to focus on operational maturity — how solutions are observed, maintained, and evolved safely in production. The next post explores what it takes to keep AI apps and agents healthy as they run, change, and scale in real customer environments. See the next post in the series: Quality and evaluation framework for successful AI apps and agents in Microsoft Marketplace | Microsoft Community Hub. Key resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor Quick-Start Development Toolkit can connect you with code templates for AI solution patterns Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV SuccessDesigning AI guardrails for apps and agents in Marketplace
Why guardrails are essential for AI apps and agents AI apps and agents introduce capabilities that go beyond traditional software. They reason over natural language, interact with data across boundaries, and—in the case of agents—can take autonomous actions using tools and APIs. Without clearly defined guardrails, these capabilities can unintentionally compromise confidentiality, integrity, and availability, the foundational pillars of information security. From a confidentiality perspective, AI systems often process sensitive prompts, contextual data, and outputs that may span customer tenants, subscriptions, or external systems. Guardrails ensure that data access is explicit, scoped, and enforced—rather than inferred through prompts or emergent model behavior. From an availability perspective, AI apps and agents can fail in ways traditional software does not — such as runaway executions, uncontrolled chains of tool calls, or usage spikes that drive up cost and degrade service. Guardrails address this by setting limits on how the system executes, how often it calls tools, and how it behaves when something goes wrong. For Marketplace-ready AI apps and agents, guardrails are foundational design elements that balance innovation with security, reliability, and responsible AI practices. By making behavioral boundaries explicit and enforceable, guardrails enable AI systems to operate safely at scale—meeting enterprise customer expectations and Marketplace requirements from day one. You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor. This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace. Using Open Worldwide Application Security Project (OWASP) GenAI Top 10 as a guardrail design lens The OWASP GenAI Top 10 provides a practical framework for reasoning about AI‑specific risks that are not fully addressed by traditional application security models. It helps teams identify where assumptions about trust, input handling, autonomy, and data access are most likely to break down in AI‑driven systems. However, not all OWASP risks apply equally to every AI app or agent. Their relevance depends on factors such as: Agent autonomy, including whether the system can take actions without human approval Data access patterns, especially cross‑tenant, cross‑subscription, or external data retrieval Integration surface area, meaning the number and type of tools, APIs, and external systems the agent connects to Because of this variability, OWASP should not be treated as a checklist to implement wholesale. Doing so can lead teams to over‑engineer controls in low‑risk areas while leaving critical gaps in places where autonomy, data movement, or tool execution create real exposure. Instead, OWASP is most effective when used as a design lens — to inform where guardrails are needed and what behaviors require explicit boundaries. Understanding risks and enforcing boundaries are two different things. OWASP tells you where to look; guardrails are what you actually build. The goal is not to eliminate all risk, but to use OWASP insights to design selective, intentional guardrails that align with the system's architecture, autonomy, and operating context. Translating AI risks into architectural guardrails OWASP GenAI Top 10 helps identify where AI systems are vulnerable, but guardrails are what make those risks enforceable in practice. Guardrails are most effective when they are implemented as architectural constraints—designed into the system—rather than as runtime patches added after risky behavior appears. In AI apps and agents, many risks emerge not from a single component, but from how prompts, tools, data, and actions interact. Architectural guardrails establish clear boundaries around these interactions, ensuring that risky behavior is prevented by design rather than detected too late. Common guardrail categories map naturally to the types of risks highlighted in OWASP: Input and prompt constraints Address risks such as prompt injection, system prompt leakage, and unintended instruction override by controlling how inputs are structured, validated, and combined with system context. Action and tool‑use boundaries Mitigate risks related to excessive agency and unintended actions by explicitly defining which tools an AI app or agent can invoke, under what conditions, and with what scope. Data access restrictions Reduce exposure to sensitive information disclosure and cross‑boundary leakage by enforcing identity‑aware, context‑aware access to data sources rather than relying on prompts to imply intent. AI Output validation and moderation Help contain risks such as misinformation, improper output handling, or policy violations by treating AI output as untrusted and subject to validation before it is acted on or returned to users. What matters most is where these guardrails live in the architecture. Effective guardrails sit at trust boundaries—between users and models, models and tools, agents and data sources, and control planes and data planes. When guardrails are embedded at these boundaries, they can be applied consistently across environments, updates, and evolving AI capabilities. By translating identified risks into architectural guardrails, teams move from risk awareness to behavioral enforcement that supports safe AI agent operation. This shift is foundational for building AI apps and agents that can operate safely, predictably, and at scale in Marketplace environments. Design‑time guardrails: shaping allowed behavior before deployment The OWASP GenAI Top 10 provides a practical framework for reasoning about AI specific risks that are not fully addressed by traditional application security models. It helps teams identify where assumptions about trust, input handling, autonomy, and data access are most likely to break down in AI driven systems. However, not all OWASP risks apply equally to every AI app or agent. Their relevance depends on factors such as: Agent autonomy, including whether the system can take actions without human approval Data access patterns, especially cross-tenant, cross subscription, or external data retrieval Integration surface area, meaning the number and type of tools, APIs, and external systems the agent connects to Because of this variability, OWASP should not be treated as a checklist to implement wholesale. Doing so can lead teams to over engineer controls in low risk areas while leaving critical gaps in places where autonomy, data movement, or tool execution create real exposure. Instead, OWASP is most effective when used as a design lens — to inform where guardrails are needed and what behaviors require explicit boundaries. Understanding risks and enforcing boundaries are two different things. OWASP tells you where to look; guardrails are what you actually build. The goal is not to eliminate all risk, but to use OWASP insights to design selective, intentional guardrails that align with the system's architecture, autonomy, and operating context. Runtime guardrails: enforcing boundaries as systems operate For Marketplace publishers, the key distinction between monitoring and runtime guardrails is simple: Monitoring tells you what happened after the fact. Runtime guardrails are inline controls—part of runtime AI safety controls—that can block, pause, throttle, or require approval before an action completes. If you want prevention, the control has to sit in the execution path. At runtime, guardrails should constrain three areas: Agent decision paths (prevent runaway autonomy) Cap planning and execution. Limit the agent to a maximum number of steps per request, enforce a maximum wall‑clock time, and stop repeated loops. Apply circuit breakers. Terminate execution after a specified number of tool failures or when downstream services return repeated throttling errors, reinforcing autonomous agent limits. Require explicit escalation. When the agent’s plan shifts from “read” to “write,” pause and require approval before continuing. Tool invocation patterns (control what gets called, how, and with what inputs) Enforce allowlists. Allow only approved tools and operations, and block any attempt to call unregistered endpoints. Validate parameters. Reject tool calls that include unexpected tenant identifiers, subscription scopes, or resource paths. Throttle and quota. Rate‑limit tool calls per tenant and per user, and cap token/tool usage to prevent cost spikes and degraded service. Cross‑system actions (constrain outbound impact at the boundary you control) Runtime guardrails cannot “reach into” external systems and stop independent agents operating elsewhere. What publishers can do is enforce policy at your solution’s outbound boundary: the tool adapter, connector, API gateway, or orchestration layer that your app or agent controls. Concrete examples include: Block high‑risk operations by default (delete, approve, transfer, send) unless a human approves. Restrict write operations to specific resources (only this resource group, only this SharePoint site, only these CRM entities). Require idempotency keys and safe retries so repeated calls do not duplicate side effects. Log every attempted cross‑system write with identity, scope, and outcome, and fail closed when policy checks cannot run. Done well, runtime guardrails produce evidence, not just intent. They show reviewers that your AI app or agent enforces least privilege, prevents runaway execution, and limits blast radius—even when the model output is unpredictable. Guardrails across data, identity, and autonomy boundaries Guardrails don't work in silos. They are only effective when they align across the three core boundaries that shape how an AI app or agent operates — identity, data, and autonomy. Guardrails must align across: Identity boundaries (who the agent acts for) — represent the credentials the agent uses, the roles it assumes, and the permissions that flow from those identities. Without clear identity boundaries, agent actions can appear legitimate while quietly exceeding the authority that was actually intended. Data boundaries (what the agent can see or retrieve) — ensuring access is governed by explicit authorization and context, not by what the model infers or assumes. A poorly scoped data boundary doesn't just create exposure — it creates exposure that is hard to detect until something goes wrong. Autonomy boundaries (what the agent can decide or execute) — defining which actions require human approval, which can proceed automatically, and which are never permitted regardless of context. Autonomy without defined limits is one of the fastest ways for behavior to drift beyond what was ever intended. When these boundaries are misaligned, the consequences are subtle but serious. An agent may act under the authority of one identity, access data scoped to another, and execute with broader autonomy than was ever granted — not because a single control failed, but because the boundaries were never reconciled with each other. This is how unintended privilege escalation happens in well-intentioned systems. Balancing safety, usefulness, and customer trust Getting guardrails right is less about adding controls and more about placing them well. Too restrictive, and legitimate workflows break down, safe autonomy shrinks, and the system becomes more burden than benefit. Too permissive, and the risks accumulate quietly — surfacing later as incidents, audit findings, or eroded customer trust. Effective guardrails share three characteristics that help strike that balance: Transparent — customers and operators understand what the system can and cannot do, and why those limits exist Context-aware — boundaries tighten or relax based on identity, environment, and risk, without blocking safe use Adjustable — guardrails evolve as models and integrations change, without compromising the protections that matter most When these characteristics are present, guardrails naturally reinforce the foundational principles of information security — protecting confidentiality through scoped data access, preserving integrity by constraining actions to authorized paths, and supporting availability by preventing runaway execution and cascading failures. How guardrails support Marketplace readiness For AI apps and agents in Microsoft Marketplace, guardrails are a practical enabler — not just of security, but of the entire Marketplace journey. They make complex AI systems easier to evaluate, certify, and operate at scale. Guardrails simplify three critical aspects of that journey: Security and compliance review — explicit, architectural guardrails give reviewers something concrete to assess. Rather than relying on documentation or promises, behavior is observable and boundaries are enforceable from day one. Customer onboarding and trust — when customers can see what an AI system can and cannot do, and how those limits are enforced, adoption decisions become easier and time to value shortens. Clarity is a competitive advantage. Long-term operation and scale — as AI apps evolve and integrate with more systems, guardrails keep the blast radius contained and prevent hidden privilege escalation paths from forming. They are what makes growth manageable. Marketplace-ready AI systems don't describe their guardrails — they demonstrate them. That shift, from assurance to evidence, is what accelerates approvals, builds lasting customer trust, and positions an AI app or agent to scale with confidence. What’s next in the journey Guardrails establish the foundation for safe, predictable AI behavior — but they are only the beginning. The next phase extends these boundaries into governance, compliance, and day‑to‑day operations through policy definition, auditing, and lifecycle controls. Together, these mechanisms ensure that guardrails remain effective as AI apps and agents evolve, scale, and operate within enterprise environments. See the next post in the series: Governing AI apps and agents for Marketplace | Microsoft Community Hub. Key resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor, Quick-Start Development Toolkit can connect you with code templates for AI solution patterns Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV Success
Marketplace offers as transactable and azure benefit eligible
Query about Marketplace listings - I want to upload a new offer as SaaS. I want to make sure it's transactable and also have the azure benefit tag added to it. I have the banking and tax profile approved and completed. I'm unable to find how do I get the Azure benefit eligible tagged to this offer (or is there a pre-requisite). I have reviewed the Marketplace best practices but unable to find how do I exactly go about doing it.Securing AI apps and agents on Microsoft Marketplace
Why security must be designed in—not validated later AI apps and agents expand the security surface beyond that of traditional applications. Prompt inputs, agent reasoning, tool execution, and downstream integrations introduce opportunities for misuse or unintended behavior when security assumptions are implicit. These risks surface quickly in production environments where AI systems interact with real users and data. Deferring security decisions until late in the lifecycle often exposes architectural limitations that restrict where controls can be enforced. Retrofitting security after deployment is costly and can force tradeoffs that affect reliability, performance, or customer trust. Designing security early establishes clear boundaries, enables consistent enforcement, and reduces friction during Marketplace review, onboarding, and long‑term operation. In the Marketplace context, security is a foundational requirement for trust and scale. You can always get a curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor. This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace. How AI apps and agents expand the attack surface Without a clear view of where trust boundaries exist and how behavior propagates across systems, security controls risk being applied too narrowly or too late. AI apps and agents introduce security risks that extend beyond those of traditional applications. AI systems accept open‑ended prompts, reason dynamically, and often act autonomously across systems and data sources. These interaction patterns expand the attack surface in several important ways: New trust boundaries introduced by prompts and inputs, where unstructured user input can influence reasoning and downstream actions Autonomous behavior, which increases the blast radius when authentication or authorization gaps exist Tool and integration execution, where agents interact with external APIs, plugins, and services across security domains Dynamic model responses, which can unintentionally expose sensitive data or amplify errors if guardrails are incomplete Each API, plugin, or external dependency becomes a security choke point where identity validation, audit logging, and data handling must be enforced consistently as part of securing AI integrations—especially when AI systems span tenants, subscriptions, or ownership boundaries. Using OWASP GenAI Top 10 as a threat lens The OWASP GenAI Top 10 provides a practical, industry‑recognized lens for identifying and categorizing AI‑specific security threats that extend beyond traditional application risks. Rather than serving as a checklist, the OWASP GenAI Top 10 helps teams ask the right questions early in the design process. It highlights where assumptions about trust, input handling, autonomy, and data access can break down in AI‑driven systems—often in ways that are difficult to detect after deployment. Common risk categories highlighted by OWASP include: Prompt injection and manipulation, where malicious input influences agent behavior or downstream actions Sensitive data exposure, including leakage through prompts, responses, logs, or tool outputs Excessive agency, where agents are granted broader permissions or action scope than intended Insecure integrations, where tools, plugins, or external systems become unintended attack paths Highly regulated industries, sensitive data domains, or mission‑critical workloads may require additional risk assessment and security considerations that extend beyond the OWASP categories. The OWASP GenAI Top 10 allows teams to connect high‑level risks to architectural decisions by creating a shared vocabulary that sets the foundation for designing guardrails that are enforceable both at design time and at runtime. Designing security guardrails into the architecture Security guardrails must be designed into the architecture, shaping where and how policies are enforced, evaluated, and monitored throughout the solution lifecycle. Guardrails operate at two complementary layers: Design time, where architectural decisions determine what is possible, permitted, or blocked by default Runtime, where controls actively govern behavior as the AI app or agent interacts with users, data, and systems When architectural boundaries are not defined early, teams often discover that critical controls—such as input validation, authorization checks, or action constraints—cannot be applied consistently without redesign: Tenancy boundaries, defining how isolation is enforced between customers, environments, or subscriptions Identity boundaries, governing how users, agents, and services authenticate and what actions they can perform Environment separation, limiting the blast radius of experimentation, updates, or failures Control planes, where configuration, policy, and behavior can be adjusted without redeploying core logic Data planes, controlling how data is accessed, processed, and moved across trust boundaries Designing security guardrails into the architecture transforms security from reactive to preventative, while also reducing friction later in the Marketplace journey. Clear enforcement boundaries simplify review, clarify risk ownership, and enable AI apps and agents to evolve safely as capabilities and integrations expand. Identity as a security boundary for AI apps and agents Identity defines who can access the system, what actions can be taken, and which resources an AI app or agent is permitted to interact with across tenants, subscriptions, and environments. Agents often act on behalf of users, invoke tools, and access downstream systems autonomously. Without clear identity boundaries, these actions can unintentionally bypass least‑privilege controls or expand access beyond what users or customers expect. Strong identity design shapes security in several key ways: Authentication and authorization, determines how users, agents, and services establish trust and what operations they are allowed to perform Delegated access, constraints agents to act with permissions tied to user intent and context Service‑to‑service trust, ensures that all interactions between components are explicitly authenticated and authorized Auditability, traces actions taken by agents back to identities, roles, and decisions A zero‑trust AI agent architecture is essential in this context. is essential in this context. Every request—whether initiated by a user, an agent, or a backend service—should be treated as untrusted until proven otherwise. Identity becomes the primary control plane for enforcing least privilege, limiting blast radius, and reducing downstream integration risk. This foundation not only improves security posture, but also supports compliance, simplifies Marketplace review, and enables AI apps and agents to scale safely as integrations and capabilities evolve. Protecting data across boundaries Data may reside in customer‑owned tenants, subscriptions, or external systems, while the AI app or agent runs in a publisher‑managed environment or a separate customer environment. Protecting data across boundaries requires teams to reason about more than storage location. Several factors shape the security posture: Data ownership, including whether data is owned and controlled by the customer, the publisher, or a third party Boundary crossings, such as cross‑tenant, cross‑subscription, or cross‑environment access patterns Data sensitivity, particularly for regulated, proprietary, or personally identifiable information Access duration and scope, ensuring data access is limited to the minimum required context and time When these factors are implicit, AI systems can unintentionally broaden access through prompts, retrieval‑augmented generation, or agent‑initiated actions. This risk increases when agents autonomously select data sources or chain actions across multiple systems. To mitigate these risks, access patterns must be explicit, auditable, and revocable. Data access should be treated as a continuous security decision, evaluated on every interaction rather than trusted by default once a connection exists. This approach aligns with zero-trust principles, where no data access is implicitly trusted and every request is validated based on identity, context, and intent. Runtime protections and monitoring For AI apps and agents, security does not end at deployment. In customer environments, these systems interact continuously with users, data, and external services, making runtime visibility and control essential to a strong security posture. AI behavior is also dynamic: the same prompt, context, or integration can produce different outcomes over time as models, data sources, and agent logic evolve, so monitoring must extend beyond infrastructure health to include behavioral signals that indicate misuse, drift, or unintended actions. Effective runtime protections focus on five core capabilities: Vulnerability management, including regular scanning of the full solution to identify missing patches, insecure interfaces, and exposure points Observability, so agent decisions, actions, and outcomes can be traced and understood in production Behavioral monitoring, to detect abnormal patterns such as unexpected tool usage, unusual access paths, or excessive action frequency Containment and response, enabling rapid intervention when risky or unauthorized behavior is detected Forensics readiness, ensuring system-state replicability and chain-of-custody are retained to investigate what happened, why it happened, and what was impacted Monitoring that only tracks availability or performance is insufficient. Runtime signals must provide enough context to explain not just what happened, but why an AI app or agent behaved the way it did, and which identities, data sources, or integrations were involved. Equally important is integration with broader security event and incident management workflows. Runtime insights should flow into existing security operations so AI-related incidents can be triaged, investigated, and resolved alongside other enterprise security events—otherwise AI solutions risk becoming blind spots in a customer’s operating environment. Preparing for incidents and abuse scenarios No AI app or agent operates in a perfectly controlled environment. Once deployed, these systems are exposed to real users, unpredictable inputs, evolving data, and changing integrations. Preparing for incidents and abuse scenarios—including AI agent incident response—is therefore a core security requirement, not a contingency plan. AI apps and agents introduce unique incident patterns compared to traditional software. In addition to infrastructure failures, teams must be prepared for prompt abuse, unintended agent actions, data exposure, and misuse of delegated access. Because agents may act autonomously or continuously, incidents can propagate quickly if safeguards and response paths are unclear. Effective incident readiness starts with acknowledging that: Abuse is not always malicious, misuse can stem from ambiguous prompts, unexpected context, or misunderstood capabilities Agent autonomy may increase impact, especially when actions span multiple systems or data sources Security incidents may be behavioral, not just technical, requiring interpretation of intent and outcomes Preparing for these scenarios requires clearly defined response strategies that account for how AI systems behave in production. AI solutions should be designed to support pause, constrain, or revoke agent capabilities when risk is detected, and to do so without destabilizing the broader system or customer environment. Incident response must also align with customer expectations and regulatory obligations. Customers need confidence that AI‑related issues will be handled transparently, proportionately, and in accordance with applicable security and privacy standards. Clear boundaries around responsibility, communication, and remediation help preserve trust when issues arise. How security decisions shape Marketplace readiness From initial review to customer adoption and long‑term operation, security posture is a visible and consequential signal of readiness. AI apps and agents with clear boundaries—around identity, data access, autonomy, and runtime behavior—are easier to evaluate, onboard, and trust. When security assumptions are explicit, Marketplace review becomes more predictable, customer expectations are clearer, and operational risk is reduced. Ambiguous trust boundaries, implicit data access, or uncontrolled agent actions can introduce friction during review, delay onboarding, or undermine customer confidence after deployment. Marketplace‑ready security is therefore not about meeting a minimum bar. It is about enabling scale. Well-designed security allows AI apps and agents to integrate into enterprise environments, align with customer governance models, and evolve safely as capabilities expand. When security is treated as a first‑class architectural concern, it becomes an enabler rather than a blocker—supporting faster time to market, stronger customer trust, and sustainable growth through Microsoft Marketplace. What’s next in the journey Security for AI apps and agents is not a one‑time decision, but an ongoing design discipline that evolves as systems, data, and customer expectations change. By establishing clear boundaries, embedding guardrails into the architecture, and preparing for real‑world operation, publishers create a foundation that supports safe iteration, predictable behavior, and long‑term trust. This mindset enables AI apps and agents to scale confidently within enterprise environments while meeting the expectations of customers adopting solutions through Microsoft Marketplace. See the next post in the series: Designing AI guardrails for apps and agents in Marketplace | Microsoft Community Hub. Key resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor, Quick-Start Development Toolkit Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV SuccessDesign CI/CD for AI apps and agents selling through Microsoft Marketplace
In the previous post, Design observability for AI apps and agents selling through Microsoft Marketplace, we focused on observability—making AI app and agent behavior visible and explainable. Execution paths, retries, degradation patterns, and agent decisions can now be observed across environments and tenants. With that visibility in place, a new challenge emerges: how do you safely modify an AI system whose behavior you can now observe? You can always get curated step-by-step guidance through building, publishing and selling apps for Marketplace through App Advisor. This post is part of a series on building and publishing well-architected AI apps and agents in Microsoft Marketplace. The series focuses on AI apps and agents that are architected, hosted, and operated on Azure, with guidance aligned to building and selling solutions through Microsoft Marketplace. Using continuous integration/continuous delivery (CI/CD) to control AI system evolution AI apps and agents introduce numerous novel ways that production behavior can change. In addition to application code, updates to configuration, prompts, models, and guardrails, agent logic can alter execution, cost, and outcomes—often immediately and across tenants. CI/CD defines how these changes reach production. Without a structured delivery path, behavior‑shaping updates risk entering runtime without validation or recovery paths, making system behavior difficult to explain or reverse once customers encounter it. AI solutions are typically built and operated as cloud applications. Software delivery of cloud services, and the supporting components that enable it, remains part of the CI/CD pipeline, and any instability in these foundational components directly propagates into AI behavior. AI systems add two additional sources of change that require explicit control. MLOps governs model evolution. Agents introduce further variability, as agent logic and configuration evolve. CI/CD is what prevents these change vectors from interacting unpredictably across both publisher and customer environments. Core CI/CD requirements for AI apps and agents For AI apps and agents, CI/CD determines whether deployment strategies can be applied safely. Progressive rollouts, ring deployments, feature flags, and kill switches all rely on pipelines that isolate change, validate behavior, and support rollback. Observability provides insight into behavior; CI/CD controls when and how that behavior is allowed to change. CI/CD must reliably provision, configure, and promote cloud native infrastructure, including but not limited to front-end services, APIs, storage, identity, and networking across environments. Agent behavior depends directly on the stability of the platform it runs on. AI systems introduce additional CI/CD requirements through MLOps and agents. Model versions, routing logic, and evaluation configurations must move through pipelines as deployable artifacts, with isolation, validation, and rollback built in. Changes to models affect latency, cost, and outcomes even when application code remains unchanged, making promotion controls necessary at the model layer. A well-run CI/CD pipeline should positively impact AI models and agents in the following ways: Change isolation ensures code, prompts, models, and configuration evolve independently. Artifact versioning beyond code treats prompts, policies, tools, and models as release assets. Behavioral validation evaluates outcomes, constraints, and patterns rather than single responses. Safe promotion controls gate model and agent releases based on observed behavior. Rollback readiness allows fast reversion when model or agent behavior degrades. Building behavioral baselines for AI solutions using CI/CD Before an AI system is built by a pipeline, it is built by a team. CI/CD build pipelines are where these contributions are stitched together. Product managers define scope and constraints. UX designers shape how behavior is experienced. Full‑stack engineers assemble application logic. AI engineers wire reasoning and tools. Data engineers and data scientists curate data and models. In AI systems, a build does more than compile code. It captures a shared agreement across roles about what the system is expected to do. Application code, orchestration logic, prompts, configuration, guardrails, routing rules, and trained or fine‑tuned models are assembled into a single versioned artifact. That artifact represents a coordinated snapshot of intent, behavior, and constraints. This coordination must declare which models, prompts, policies, and tool definitions are included. Implicit dependencies—such as dynamically changing prompts or unpinned models—break shared understanding across teams and introduce behavior changes without acknowledgement. A successful build confirms that contributions from multiple roles are compatible and executable together. It does not decide when customers see the change. That decision belongs later, where behavior can be evaluated deliberately, enforced by build pipelines that are separating assembly from release. Testing AI solutions with CI/CD pipelines When an agent is updated, the first task is straightforward: the agent’s code changes. Logic is refined, tools are added, limits are adjusted. That change moves through the CI/CD pipeline, where it is built, packaged, and validated in isolation. At this point, the focus is narrow—does this agent compile, configure, and execute as expected? The second step widens the lens. The update now moves through testing aligned to the layers beneath it. For cloud solutions, tests confirm the platform still behaves as assumed: infrastructure provisions correctly, APIs and identity boundaries remain intact, and dependencies remain reachable. These tests ensure the environment can support execution before behavior is evaluated. Next, MLOps tests assess whether model behavior still aligns with system expectations. New model versions, routing logic, or provider changes are evaluated for cost, latency, and outcome consistency. The goal is not identical responses, but bounded behavior within known limits. Finally, testing shifts to the agentic system as a whole. Other agents need to be made aware of the new capabilities. When you go to update the agent the first job you have to do is update the agent code. The second job you have to do is to use your CI/CD pipeline to build, test and release that code. The third job is to test that the entire agentic system is running smoothly together. At this stage, testing answers a different question: not does the agent work, but does the system still work together. CI/CD release management as team coordination Once testing confirms that behavior remains within expected bounds, release management determines how changes are introduced and observed under real conditions. In AI systems, release management must reflect where change originates and how risk propagates across layers. Within the cloud services that support the AI solution, release management focuses on scope and blast‑radius control. Examples include staged rollout of infrastructure updates, controlled exposure of new API versions, and limiting configuration changes to specific environments or tenants before going global. These steps allow both publisher and customer teams to observe stability and dependency behavior under load. For MLOps, release management governs behavioral shifts introduced by model changes. Common patterns include routing a small percentage of requests to a new model version, limiting exposure to specific customer segments, or restricting usage to defined request types. This allows teams to compare cost, latency, and outcome patterns before expanding exposure. For agents, release management controls how new behaviors surface. Prompt updates, tool access changes, or guardrail adjustments may be released to specific workflows, tenants, or traffic slices. This makes it possible to observe planning depth, retry behavior, and termination patterns without affecting all users simultaneously. Rollback readiness remains essential. Release paths must allow fast reversion using version pinning or traffic shifting rather than full redeployment. Release management creates space to observe, adjust, and respond before changes reach full Marketplace scale. Deployment as a shared boundary Effective deployment pipelines ensure that software, models, and agent behavior enter production together, with changes explicitly acknowledged and observable. Versioning and rollback remain available, but deployment defines the moment when coordinated decisions become customer‑visible. Cloud service—For the software, deployment governs application code and supporting platform changes. These remain necessary foundations. Application binaries, infrastructure templates, runtime configuration, and orchestration must enter production in a known, versioned state so operational behavior can be correlated with specific changes. MLOps—Model version updates, routing rules, provider switches, and evaluation configurations can change system behavior without modifying application code. Deployment pipelines must therefore treat these artifacts as deployable units, subject to the same versioning, promotion, and rollback mechanics as software releases. Agent—Deployment includes behavior‑defining inputs such as prompts and system messages, tool definitions and permissions, guardrails, and execution limits. Changes directly affect how agents plan, execute, and terminate work. Allowing these inputs to change outside deployment pipelines breaks traceability and weakens accountability across teams. How CI/CD best practices positively impact marketplace readiness Customers expect updates to arrive in predictable ways. They expect that behavior changes can be explained, that issues can be reversed without prolonged disruption, and that outcomes remain consistent across trials and production use. CI/CD pipelines make these expectations achievable by ensuring changes are versioned, staged, and observable as they move through environments. Reliability depends on limiting how far unstable behavior propagates. Billing accuracy depends on knowing when changes alter execution paths, token usage, or metering logic. Compliance depends on being able to identify which versions of software, models, and agent configurations were active at a given time. Offer type shapes how CI/CD is applied. For transactable SaaS offers, CI/CD operates entirely within the publisher’s environment. For container offers and Azure Managed Applications, deployment boundaries extend to customer environments requiring a CI/CD hand-off between publisher and customer pipelines. Publisher CI/CD responsibilities for AI solutions Publishers must define what constitutes a deployable change. Updates to software, models, prompts, agent configuration, guardrails, or limits should not enter customer environments or generally available code implicitly. Each change that can influence behavior must flow through the publisher’s CI/CD pipelines so it can be versioned, observed, and reversed if necessary. Additionally, CI/CD pipelines require validation and approval before promotion, ensuring that behavior‑altering updates do not reach customers without visibility or control. Publishers are also responsible for communicating behavior changes. Customers should be able to understand when updates affect outcomes, performance, or cost profiles. Customers should never experience silent behavior shifts, undocumented updates, or releases that cannot be recovered cleanly. When those occur, trust erodes quickly. In this context, CI/CD is part of how publishers establish reliability, accountability, and trust with Marketplace customers. Customer’s responsibility: CI/CD across environments (Dev / Stage / Prod) While publishers own CI/CD pipelines, customers play an important role in how AI systems are evaluated and adopted across environments. AI behavior often manifests differently across Dev, Stage, and Prod because operating conditions change as systems move toward real usage. As environments scale, dependency interactions increase, traffic patterns diversify, and tenant behavior becomes less predictable—revealing execution paths and constraints that are not exercised earlier. These differences affect how behavior appears during evaluation and rollout. To keep behavior interpretable across environments, pipeline structure matters. CI/CD pipelines, validation steps, and promotion criteria should operate consistently so signals observed earlier can be understood later. When these mechanics diverge between environments, it becomes difficult to attribute changes in behavior to specific updates or conditions. Staging environments serve as a behavioral proving ground. They allow customers to observe retries, limits, degradation paths, and cost behavior under conditions that more closely resemble production. Trials often run against production‑like configurations, which means CI/CD gaps surface early. When behavior differs from expectations, the consistency of pipelines determines how quickly teams can diagnose and respond. What’s next in the journey With CI/CD establishing control over how AI systems change, the next focus is how those changes are introduced safely at runtime. The following posts cover deployment strategies, progressive rollouts, and operational patterns that allow AI apps and agents to evolve while remaining stable, observable, and ready for Marketplace scale. Key resources See curated, step-by-step guidance to help you build, publish, or sell your app or agent (no matter where you start) in App Advisor Quick-Start Development Toolkit can connect you with code templates for AI solution patterns Microsoft AI Envisioning Day Events How to build and publish AI apps and agents for Microsoft Marketplace Get over $126K USD in benefits and technical consultations to help you replicate and publish your app with ISV Success
