Quality 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 Success2026 Microsoft Marketplace Partner of the Year Award – nomination window is open!
We want to recognize our amazing partners that have embraced Microsoft Marketplace as core to their GTM strategy and joint selling opportunities. Get ready to share your outstanding achievements and become an inspiration to the whole Microsoft partner community! Submit your nomination for a chance to be recognized in the Microsoft Partner of the Year Awards. The nomination window for the 2026 Microsoft Partner of the Year Awards is open now through July 7, 2026. Submit your nomination soon for a chance to be recognized as the Microsoft Marketplace Partner of the Year! About the Marketplace Partner of the Year Award: Recognizes a software development company that has successfully adopted Marketplace as core to their go-to-market and joint-selling opportunities with Microsoft. Strong nominations will demonstrate how the software company is helping customers solve challenges and achieve business goals with their solutions offered through Marketplace. Successful entries will also include quantifiable success through growth in customer acquisition, Marketplace billed sales (MBS), Azure consumption revenue (ACR), and/or M365 Copilot usage. Competitive submissions will demonstrate effective utilization of Marketplace features such as private offers, multiparty private offers, and/or CSP private offers. Preferred qualifications: Publicly available offer that is transactable and surfaced on Marketplace Azure IP co-sell eligible offer(s) or creative use of co-selling alongside other Microsoft technologies like Microsoft 365 Copilot Use of Marketplace across multiple channels (i.e., digital direct, channel sales, co-sell) Call to Action Prepare your nomination & submit before July 7, 2025! Visit https://aka.ms/POTYA for more details. Additional resources: Complete award guidelines: https://aka.ms/POTYA_Guidelines Guidance from the judges: https://aka.ms/POTYA_JudgesGuidance Frequently asked questions: https://aka.ms/POTYA_FAQCodeCargo, Centric, Clerk Chat, and CrashPlan deliver transactable offers in Microsoft Marketplace
Microsoft partners like CodeCargo, Centric, Clerk Chat, and CrashPlan deliver transact-capable offers, which allow you to purchase directly from Microsoft Marketplace. Learn about these offers in this blog post.Rethinking cloud gateways for apps, AI, APIs, and more in Azure with F5 in Microsoft Marketplace
In this guest blog post, Ilya Krutov, Senior Product Marketing Manager at F5, considers the evolution of cloud architectures and how F5 NGINXaaS for Azure in Microsoft Marketplace unifies delivery, security, and governance, helping platform teams avoid building yet another siloed control plane for AI.
