What's preventing your Marketplace growth engine from scaling?
Many SDCs know where they want to go with Marketplace, but struggle to turn individual activities into a repeatable growth motion. The latest in the Sell more with App Advisor blog series, asks: ✅ What part of your Marketplace journey is most repeatable today? ✅ Where do you still rely on manual effort or tribal knowledge? ✅ If you could eliminate one friction point between building, publishing, and growing, what would it be? Read the blog and share your thoughts: Build a Repeatable Marketplace Growth Engine with App Advisor The responses could generate useful insights for future Marketplace content and help identify common challenges across the community!Build a repeatable Marketplace growth engine with App Advisor to sell more
Publishing your offer is only the beginning. Learn how App Advisor helps software development companies build a repeatable Marketplace growth engine with curated go-to-market guidance, Marketplace Insights, AI-powered optimization, and proven best practices that drive long-term visibility, engagement, and revenue growth.What are your frictions points between build and revenue?
Turning a great product into Marketplace revenue isn't always straightforward. The latest post in the Sell More with App Advisor blog series highlights How to use App Advisor to avoid 5 common mistakes and sell more on Marketplace Many software companies have the technical capability to build great solutions, but still encounter delays caused by fragmented guidance, uncertainty about next steps, or difficulty navigating Marketplace programs and processes. That's exactly the challenge App Advisor is designed to address. 💬 Where does your team encounter the most friction today? Choosing the right Marketplace offer type? Finding the right architecture patterns or code samples? Publishing and optimizing listings? Understanding co-sell and negotiated deal opportunities? Scaling revenue after launch? If you haven't explored it yet, check out the latest post in our Sell More with App Advisor series and join the conversation.Clarity at every stage: App Advisor turns Marketplace complexity into action
For software development companies, the Microsoft Marketplace journey isn’t always linear. It’s a series of decisions: what to build, how to package it, how to publish, and how to sell it, each with real dependencies and real friction.Why some apps reach revenue faster than others—and how App Advisor helps close the gap
Your team is working hard to build powerful apps, agents, AI solutions, and industry-specific offerings designed to solve real customer problems. The challenge is getting those solutions to market—and generating revenue at scale—as quicklyQuality 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 SuccessWhy governance is essential for scaling AI apps and agents in Microsoft Marketplace
As AI apps and agents become more autonomous and integrated across enterprise environments, governance is no longer a secondary consideration—it is foundational to building solutions customers can confidently adopt and operate at scale. In this Microsoft Marketplace blog, learn how governance transforms powerful AI capabilities into controlled, accountable solutions by establishing responsibility for system actions, defining acceptable behavior boundaries, and enabling ongoing review and auditability. The article outlines how effective governance for AI apps and agents spans three core dimensions—policy, enforcement, and evidence—ensuring that AI behavior in production environments remains intentional, explainable, and aligned with customer expectations. For software development companies building and publishing AI-powered solutions through Microsoft Marketplace, readiness is increasingly defined not by raw technical capability, but by control, accountability, and trust in real-world deployment scenarios. If you’re designing, publishing, or scaling AI solutions through Microsoft Marketplace, this guidance can help you strengthen enterprise trust and ensure your apps and agents are built for long-term operational success. Read the full article: Governing AI apps and agents for Marketplace | Microsoft Community HubJoin Marketplace at Microsoft Build!
The Marketplace team will be at Microsoft Build, June 2-3 in San Francisco, CA! We hope you'll join us in the Hub to meet with experts on how to build, publish, and monetize apps and agents with Microsoft Marketplace. "Favorite" the Marketplace lightning talk which covers the start-to-finish publishing process and highlights benefits and incentives available from Microsoft for software developers: Monetize apps and agents with Microsoft Marketplace Check out the full catalog to explore sessions across the topics: Cloud Platform & Data, Developer Tools & Frameworks, Apps & Agents, Model Training, Windows, and Responsible AI. Can't make it to San Francisco? You can always register for the digital experience. See you there!
