Getting first customers after publishing what actually worked for you?
I published two transactable SaaS offers earlier this month (Microsoft 365 security and compliance assessment). The technical side went fine, with good support from the ISV team. Getting the offers in front of customers is where I'm stuck. So far I've reached Rewards Tier 3 and activated the listing optimisation benefit. I understand customer references are the gate to co-sell readiness, which feels like a chicken-and-egg problem when you have none yet. For publishers further along than me: what actually produced your first few customers? Was it private offers to targeted tenants, co-sell once you had references, the Marketplace Rewards activities, or something outside the marketplace entirely? I'd rather spend my limited time on what works than keep guessing.Microsoft Marketplace Partner Digest - September 2026
Marketplace innovations are simplifying every stage of the customer journey. Microsoft Marketplace continues to make it easier for customers to discover, purchase, and manage the solutions that power their businesses. This month, we're highlighting new AI-powered discovery experiences that help customers find and evaluate solutions faster, innovative healthcare opportunities through Dragon Copilot Physician apps and agents, and new capabilities that support procurement, billing, and subscription management at scale. Together, these investments help reduce friction across the customer journey while creating new opportunities for partners to grow through Marketplace. ⨠Find the right solutions faster with intelligent discovery What's new Microsoft Marketplace now includes intelligent discovery, an AI-powered experience that helps customers discover, compare, and evaluate solutions using natural language rather than traditional keyword searches. Customers can describe their business needs in everyday language and receive tailored recommendations, side-by-side product comparisons, and deeper product analysis to support purchasing decisions. Key capabilities include: Use natural language to describe requirements and business goals Automatically identify relevant filters and narrow results Compare solutions side-by-side based on Marketplace metadata Explore solution details using AI-assisted product analysis Move from discovery to evaluation with greater confidence Why it matters Customers increasingly expect AI-assisted buying experiences. Intelligent discovery helps buyers navigate a growing Marketplace catalog more efficiently, reducing the time required to find relevant solutions and evaluate alternatives. This can help partners improve visibility, attract more qualified prospects, and create stronger pathways from discovery to purchase. Marketplace AI offerings continue to grow rapidly, with more than 6,500 AI apps and agents now available in the catalog. Recommended action Review your Marketplace listings to ensure product descriptions, categories, industries, use cases, and solution metadata accurately reflect the problems your solution solves. High-quality Marketplace content helps intelligent discovery surface your offerings to the right customers. đ Learn more about searching and comparing products with intelligent discovery in Microsoft Marketplace ⥠Help customers align Marketplace purchases of your products with their internal procurement and financial processes Whatâs new Purchase order management and spend mapping capabilities are now generally available in Microsoft Marketplace, helping organizations associate Marketplace purchases with internal procurement identifiers and financial reporting requirements. These capabilities help customers connect Marketplace transactions to purchase orders, cost centers, departments, business units, and other financial controls. Key capabilities include: Associate Marketplace purchases with purchase orders Map Marketplace spend to internal financial identifiers Improve chargeback and allocation reporting Support invoice reconciliation and procurement processes Increase visibility into Marketplace spending against budgets Why it matters Customers consistently identify invoicing, procurement integration, spend visibility, and financial governance as important considerations when scaling Marketplace purchasing. Helping customers align Marketplace transactions with existing procurement workflows can reduce operational friction and support broader Marketplace adoption. Recommended action Review customer-facing sales materials and deployment discussions to understand how purchase orders, spend mapping, and financial governance capabilities may support procurement, finance, and FinOps stakeholders during the buying process. đLearn more about purchase orders and allocating Marketplace spend in Azure portal đReach healthcare customers through Dragon Copilot What's new Microsoft Marketplace now supports Dragon Copilot Physician Apps and Agents, the first offer type in a planned family of Dragon Copilot offers. Available in the United States, these partner solutions can be discovered and purchased through Microsoft Marketplace, configured through the Dragon Admin Center, and surfaced directly within physician workflows in Dragon Copilot. Key capabilities include: Publish healthcare-focused apps and agents through a dedicated Marketplace offer type Reach healthcare organizations adopting Dragon Copilot and deliver capabilities directly within clinician workflows Utilize Microsoft Marketplace for commerce, discovery, and fulfillment Why it matters Healthcare organizations are looking for AI solutions that fit naturally into existing clinical workflows. Dragon Copilot Physician apps and agents give partners a scalable route to market: solutions can be purchased through Microsoft Marketplace, configured through the Dragon Admin Center, and made available directly within clinical workflows. Recommended action Evaluate whether your healthcare solution could benefit from integration with Dragon Copilot and explore the new Marketplace offer type for physician apps and agents. Learn more about Dragon Copilot Physician apps and agents Events Recent events Announcing Frontier Accelerate for Marketplace Frontier Accelerate for Marketplace is an upcoming Microsoft AI Cloud Partner Program offering that guides software development companies to build AI-powered solutions, monetize through Microsoft Marketplace, and scale revenue through Microsoft expertise, skilling, and investments. đş Watch the briefing now đ Learn more: aka.ms/FA-for-Marketplace How leading partners drive co-sell success with Marketplace Learn how successful software companies use Microsoft Marketplace to expand reach, strengthen co-sell relationships, and drive repeatable revenue growth. Join Reis Barrie of Carve Partners for insights into building Marketplace momentum and creating co-sell opportunities across the Microsoft partner ecosystem. Watch on-demand Evaluate and buy solutions with Microsoft Marketplace Help customers evaluate and buy solutions with greater confidence. This Microsoft Marketplace session explores intelligent discovery, free trials, private offer requests, flexible contract durations, and practical ways partners and sellers can create purchasing arrangements aligned to customer priorities. Watch on-demand Upcoming events New monthly partner skilling series đBeginning Sept. 23, 2026, 8:00 AM and 5:00 PM PDT Microsoft is launching Partnering for Success Together, a new monthly partner skilling series designed to help partners stay up to date on Microsoft's latest investments, go-to-market priorities, and customer opportunities. Hosted through the Microsoft Partner Skilling Hub, each session provides guidance and updates across Microsoft's commercial solution areas, helping partners understand what's new and how to translate announcements into customer impact. Two live sessions will be provided at different times to accommodate global audiences. Register for the first event scheduled September 23 and add future monthly skilling events to your development plan. Register now on the Microsoft Partner Skilling Hub Microsoft co-sell playbook: From co-sell ready to close-won đ Wednesday, Oct 07, 2026, 8:30 AM PDT Join this partner-focused session to learn how successful organizations turn co-sell opportunities into closed-won business. Kevin Flitcroft of Spektra Systems shares proven strategies for stakeholder alignment, commercial readiness, Microsoft Marketplace positioning, and customer-focused sales execution. Get the details and add to your calendar Next steps Position your solution for AI-powered discovery The Marketplace experience continues to evolve to help customers discover, evaluate, and purchase solutions more efficiently. By improving how your solution is represented in Marketplace and taking advantage of new Marketplace capabilities, you can increase visibility, engage more qualified buyers, and accelerate growth through Microsoft's commercial ecosystem. Not sure where to start? Get guidance and recommendations through App Advisor.
Are Shopify merchants using Dynamics 365 and Microsoft 365 Copilot a meaningful market segment?
Hi Marketplace community, We are testing a market assumption before investing further in a Microsoft 365 Copilot solution for retail loyalty operations. The solution, Loyalty Manager Copilot AI, helps teams review loyalty-program health and take bounded, reviewable actions, such as approving a win-back reward, from within a Copilot conversation. Some merchants we encounter use Shopify or Shopify Plus for the storefront, while Microsoft systems support parts of the back office. Readers who want to understand or try the current app can https://marketplace.microsoft.com/en-us/product/WA200010024. We know the technical pattern exists: Shopify at the storefront, with Dynamics 365 Business Central or other Microsoft business applications behind it. What We are trying to understand is whether the user and buying pattern is large enough to matter. For retailers using Shopify together with Dynamics 365 and Microsoft 365: Are loyalty, CRM, or marketing users working in Teams and Microsoft 365 Copilot regularly? Is Copilot adoption reaching these business roles, or is it still concentrated in IT, finance, and administration? Who usually evaluates or sponsors a cross-platform loyalty solution: the merchant, a Microsoft partner, a Shopify partner, or another stakeholder? Would an AppSource solution connecting loyalty operations to Copilot fit an established workflow, or would it require creating a new behavior? We would especially value firsthand observations from merchants, ISVs, system integrators, and Microsoft partners. We are not looking for a product pitch. We are trying to determine whether this represents a meaningful audience or a narrow edge case.Marketplace Publishing - Email with the Certification Team
Greetings partners, fellows, and friends, Standards Core is a 2nd year ISV - we currently have 1 SaaS and 2 Office add-in listings, now working on our first Apps and agents for Microsoft 365 and Copilot ... we have submitted our first publish, can currently working to address certification details. Why am I posting this? Well, after the 1st submission, in addition to the certification report on Marketplace, we also received an email - thereafter questions/iterations were immediately interactive with very quick and clearly attentive responses. So I wanted to mention that we really appreciate that!! Previous submissions that I have experienced were through the submissions portal only - the personalized email interaction was a pleasant surprise, and made the process feel much warmer than the previous "formal" portal-only interaction. Thank you [certification team], and looking forward to re-submit shortly. Thanks again, Leon, CTO/Standards Core LLCDecember 2025
Microsoft Ignite 2025 - Marketplace highlights Microsoft Ignite was packed with announcements and insights for Marketplace partners. From new commerce capabilities to AI-driven innovations, here are some key takeaways: Global expansion of Microsoft Marketplace - Microsoft announced that the reimagined Microsoft Marketplace, which launched in the U.S. earlier this year, is now globally available. This expansion includes new APIs for distribution partners, enabling them to link their own cloud marketplace with Microsoftâs, opening significant opportunities for software companies in SMB and mid-market segments. đŹ Watch a recorded webinar with TD SYNNEX on the power of distribution to accelerate SMB marketplace sales. Global availability of Resale Enabled Offers - This capability allows software development companies to and channel partners to resell software solutions directly through Marketplace, simplifying transactions, expanding reach, and scaling revenue. đ Read more about this announcement and get started Introducing App Accelerate - A unified offer that brings together incentives, benefits, and co-sell support across the Microsoft Cloud. App Accelerate provides end-to-end technical guidance, developer tools, and go-to-market resources so software development companies can innovate and scale. Previews are beginning now, with full availability planned for 2026. â Sign up to receive updates Enhanced Partner Marketing Center - Discover, customize, and launch campaigns faster with intelligent search and AI-powered toolsâall on one connected platform. The current Partner Marketing Center will remain available as the new and enhanced Marketing Center platform launches in early 2026 with 24 campaigns-in-a-box, aligned to FY26 solution plays. ⨠Get ready for the new era of partner marketing Frontier Partner badge â New customer-facing badges recognize top services, channel, and software development company partners that are driving AI transformation with customers and offer them an opportunity to differentiate themselves from the competition. đĄď¸Differentiate your AI-first leadership Catch up on Microsoft Ignite sessions Ignite 2025 delivered powerful insights and announcements for Marketplace partners, and now you can catch up on the sessions you missed. Explore these recorded keynotes to learn about new capabilities, partner programs, and strategies to accelerate growth through Microsoftâs ecosystem. Ignite opening keynote Ignite partner keynote: Powering Frontier Partnerships Additionally, weâve compiled recordings of relevant Marketplace partner and customer sessions so you can watch on-demand. Revisit Marketplace-focused sessions and resources. Just look for the ⨠icon below. Partner sessions: PBRK415 Grow your business with Microsoft AI Cloud Partner Program Find out how the Microsoft AI Cloud Partner Program helps you grow with new benefits, designations, and skilling opportunities. This session covers updates like the Frontier Partner Badge, Copilot specialization, and streamlined Marketplace engagementâall designed to accelerate your AI transformation journey. PBRK416 Accelerate Growth through Partner Incentives Explore how Microsoft is boosting partner growth with streamlined incentives, AI-first strategies, and new designations like Frontier Distributor. This session covers expanded investments in Azure Accelerate, Copilot solutions, and security practicesâplus insights on how to capitalize on evolving programs and co-sell opportunities. PBRK417 Partner: Connect, Plan, Win â Enhancing Co-sell Engagement Discover how to enhance collaboration, optimize joint efforts, and drive success in shared initiatives. Gain insights into improving interactions with Microsoft sellers and leveraging opportunities, along with guidance on proactive co-selling to align your goals with Microsoft's for sustained growth. PBRK418 Partner: Benefits for Accelerating Software Company Success Learn about the resources and benefits available for software development companies across all stages of the build, publish and grow journey in MAICPP. Whether youâre developing a new agent solution or working toward a certified software designation, there are targeted skilling opportunities, technical resources, and GTM benefits to help. Tap into new investments for AI apps and agents and hear from your peers on how theyâve used rewards such as customer propensity scores and Azure sponsorship. PBRK419 SI & Advisory Partner Readiness: Accelerating the Journey to Frontier Understand how Microsoft is empowering our SI and advisory partners to accelerate frontier firm readiness for our Enterprise customers by driving AI transformation with agentic solutions and services. â¨PBRK420 Executing on the channel-led marketplace opportunity for partners See how Microsoftâs unified Marketplace drives partner growth with resale-enabled offers, creating scalable channel sales and co-sell opportunities. This session shares practical steps to build a sustainable Marketplace practice and leverage the partner ecosystem for greater reach and profitability. PBRK421 Enabling a thriving partner ecosystem: New CSP Authorization Criteria Dive into whatâs new for Cloud Solution Providers, including updated authorization requirements and designations that help you stand out. This session covers steps to choose the right tier, build trust as a customer advisor, and prepare for growth with AI-driven solutions and Copilot offerings. PBRK422 The Future of Partner Support: Customer + Partner + Microsoft Discover âUnified for Partners,â Microsoftâs new support model designed for CSP partners to deliver customer success at scale. This session introduces the Support Services designation, offering faster response times, financial incentives, and integrated tools to strengthen your support capabilities. PBRK423 Partner Execution at Scale with SME&C Explore growth opportunities in the high-potential SME&C segment. This session highlights investments in co-selling, AI-first strategies, and what it means to become âcustomer zero,â with examples of frontier firms driving innovation at scale. â¨PBRK424 Marketplace Success for Partnersâfrom SMB to Enterprise Learn how to build, publish, and monetize AI-powered solutions through Microsoft Marketplace. This session shares a proven approach to align your Marketplace strategy with your sales motion and unlock new revenue opportunities. PBRK272 Accelerate Secure AI: Microsoftâs Security Advantage for Partners Explore Microsoftâs integrated security solutions and learn how to help customers strengthen their defenses in the AI era. This session highlights partner opportunities, resources to grow your security practice, and what it takes to lead as a next-generation security partner. Customer Sessions: â¨Microsoft Marketplace: Your trusted source for cloud solutions, AI apps, and agentsâ | STUDIO47 Hear from Cyril Belikoff, VP of Commercial Cloud & AI Marketing, sharing the reimagined Microsoft Marketplaceâthe gateway to thousands of AI-powered apps, agents and cloud solutionsâall built to accelerate innovation and drive business outcomes. Discover how customers benefit from faster deployment, seamless integration with Microsoft tools, and trusted solutions, and how partners can scale their reach, accelerate sales, and tap into Microsoftâs global ecosystem. Azure Accelerate in action: Confidently migrate, modernize, and build faster Join Cyril Belikoff for a rapid Q&A that spotlights real-world customer success and the transformative impact of Azure Accelerate. Hear how customers like Thomson Reuters achieved breakthrough results with our powerful offering that provides access to Microsoft experts and investments throughout your Azure and AI journey. â¨BRK213 Microsoft Marketplace: Your trusted source for cloud and AI solutions Discover how the reimagined Microsoft Marketplace is reshaping the future of cloud and AI innovation. In this session, weâll explore how Microsoft Marketplaceâunifying Azure Marketplace and Microsoft AppSourceâempowers organizations to become Frontier Firms by streamlining the discovery, purchase, and deployment of tens of thousands of cloud solutions, AI apps, and agents. â¨BRK215 Boost cloud and AI ROI using Microsoft Marketplace As organizations embrace an AI-first future, cloud adoption is accelerating to drive innovation and efficiency. This session explores practical strategies to optimize cloud investmentsâbalancing performance, scalability, and cost control. Learn how Microsoft Marketplace enables rapid solution deployment while maintaining governance, compliance, and budget discipline. Build a resilient, cost-effective cloud foundation that supports AI and beyond. Community Recap Partner of the Year Award Winners Congratulations to the winners and finalists of the 2025 Microsoft Partner of the Year Awards in the Marketplace category! đ Explore all winners and finalists Fivetran earned the top honor as Marketplace Partner of the Year for its innovation in automating data movement on Microsoft Azure, enabling enterprises to accelerate AI and analytics initiatives. Varonis Systems Inc. and Bytes Software Services were recognized as finalists for delivering exceptional solutions and driving customer success through Marketplace. Whatâs Coming Up AI-powered acceleration: Scale faster in Microsoft Marketplace đ Thursday, December 04, 2025, at 9:00 AM PST Microsoft Marketplace is no longer just a procurement convenience; itâs a strategic revenue engine. Dive into operational readiness, CRM-native automation, seller engagement, trust signals, and AI-enabled acceleration. Whether you're just getting started or looking to optimize your Marketplace motion, this session will provide you with information that will turn your first sale into a repeatable growth engine. Scale smarter: Discover how resale enabled offers drive growth đ Friday, December 05, 2025, from 11:00 - 12:00 PM GTM+1 Discover how resale enabled offers help software development companies to scale through the Microsoft Marketplace by simplifying transactions, expanding reach and accelerating co-sell opportunities. Chart your AI app and agent strategy with Microsoft Marketplace đ Thursday, December 11, 2025, from 8:30 - 9:30 AM PST Organizations exploring AI apps and agents face a critical choice: build, buy, or blend. Thereâs no one-size-fits-allâeach approach offers unique benefits and trade-offs. Tune in for insights into the pros and cons of each approach and explore how the Microsoft Marketplace simplifies adoption by providing a single source for trusted AI apps, agents, and models. Office hours for partners: Marketplace resale-enabled offers đ Thursday, December 18, 2025, at 8:30 AM PST Tune in to explore resale enabled offers through Microsoft Marketplace. This recently announced capability enables software companies to expand into new markets globally, at scale, and without additional operational overhead. Dive deep into the workflow and requirements for these deals. Learn about reporting and best practices from those that are already selling globally with resale enabled offers. Microsoft Ignite will return to San Francisco next year đ November 17-20, 2026 Sign up now to join the Microsoft Ignite early-access list and be eligible to receive limitedâedition swag at the event. đŹ Share Your Feedback! We truly appreciate your feedback and want to ensure these Partner Digests deliver the information you need to succeed in the marketplace. If you have any feedback or suggestions on how we can continue to improve the content to best support you, weâd love to hear from you in the comments below!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 failureâand 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 nowâand 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 usageâespecially 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. Tenantâlevel 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 understandingâsupporting 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 customerâs 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: Usageâbased 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 beginsâAPI 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 choicesâplan, call a tool, retry, exit earlyârather than a single requestâresponse 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 agentâdriven, observability needs to move closer to where decisions are made. Agents introduce variability by design. They plan, adapt, and choose workflow paths dynamically. Without firstâclass 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 themâand where that freedom begins to stretch into inefficiency. Observability follows how the agent did its job instead of treating the agentâs 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 deterministicallyâmeasuring execution, enforcing boundaries, and clarifying outcomes. When those roles stay separate and wellâinstrumented, 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 endâtoâend execution. Observability centers on agent behavior, workflow completion, token usage, latency, and dependency impact across tenants. Publishers rely on consistent signalsâoften surfaced through tools like Azure Monitor, Application Insights, and Microsoft AI Foundryâto explain how requests behave as scale and load increase. For containerâbased 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 evaluationâwhen 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 ownâand 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 behaviorâhow requests progressed, where execution concluded, and whyârather 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 environmentâlevel 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 customerâs 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 usageâbased 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 realâworld 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. Whatâs 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 foundationâensuring 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 wellâdesigned 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 strategyâDev, Stage, and Productionâis 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 aâŻseriesâŻon building and publishing well-architected AI apps and agents in Microsoft Marketplace.âŻThe 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. AIâdriven 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 wellâdesigned 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 threeâtier 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. Featureâflagâdriven experimentation, enabling controlled testing. Staging environment: Validate behavior before promotion Stage is where quality gates activate. All changesâincluding prompt updates, model upgrades, new tools, and logic changesâmust 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 environmentâspecific 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. Customerâcontrolled promotion workflowsâupdates should never be forced. No required reâcreation of environments for each new version. Publishers should design deployment artifacts such that customers do not have to manually reâestablish trust boundaries, identity settings, or configuration details each time the publisher releases a solution update. Plan for AIâspecific 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 Publisherâcustomer 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 environmentâspecific 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 wellâdefined 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. Whatâs 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 followâup post will explore tenant linking, OAuth consent patterns, and identityâplane 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 â and why it matters for Marketplace Traditional software quality spans many dimensions â from performance and reliability to correctness and fault tolerance â 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 contextâdependent. 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 â 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 â 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 â 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 aâŻseriesâŻon building and publishing well-architected AI apps and agents in Microsoft Marketplace.âŻThe 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 â with documented evaluation frameworks, defined release criteria, and evidence of ongoing measurement â 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?" â 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 â 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 â Microsoft's collection of human-AI interaction design patterns â 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 â 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 â not treated as universally authoritative. Safety and alignment â 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 â and that users can identify and address issues before they cause downstream harm. Consistency and reliability â 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 â 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 â not just what they literally typed. These dimensions work together â 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 â 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 â alongside the fitness-for-purpose dimensions defined in the previous section. How to measure it â 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 â 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 â 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 â 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 â 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 â each designed to surface a different category of risk, at a different stage of the solution lifecycle. Automated metric evaluation â 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 â for example, a minimum task adherence pass rate before a release proceeds. Safety evaluation â 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 â 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 â 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 â 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 â 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 â 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 â 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 â 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 â the record of what was measured, when, and against what criteria â 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 â 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 â 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 â 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 â 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 Success
