Quality and evaluation framework for successful AI apps and agents in Microsoft Marketplace

Julio_Colon

Microsoft

Apr 13, 2026

Building AI apps and agents for Microsoft Marketplace requires a different definition of quality—one that accounts for non-deterministic behavior, evolving context, and real-world risk. This post explains how quality measurement turns those complexities into something observable, enabling publishers to ship with confidence, pass Marketplace review, and earn customer trust.

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. 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. Quality measurement is what turns those expectations into something observable — and that visibility is what determines Marketplace readiness.

This post is part of a series on building and publishing well-architected AI apps and agents on 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 approaches — 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.
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. 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.