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

When we talk about traditional software quality, it encompasses various aspects, including performance, reliability, correctness, and fault tolerance. Once these features are clearly defined and validated, the system typically behaves in a stable and predictable manner. Quality is judged based on correctness, reliability, performance, and how well it meets specifications.

AI applications and agents, however, introduce a new way of thinking. Their behaviour is often unpredictable and depends heavily on context. The same input might yield different responses due to factors like model version, previous interactions, or even environmental conditions. For AI systems that can make choices, quality is based on the reasoning paths they take, the tools they select, and how they carry out decisions over several steps, not just on the final result.

This means an AI application may seem to function correctly while still lacking quality, leading to responses that are inconsistent, misleading, poorly aligned with user intent, or unsafe in certain situations. Without a proper evaluation framework, these quality gaps often only emerge once the application is in a live setting, causing issues after users have already placed their trust in it.

This distinction is crucial for Microsoft Marketplace. Buyers expect AI applications and agents to behave consistently, operate within defined boundaries, and remain suitable for their intended purpose as they expand. Measuring quality translates these expectations into something tangible, which is essential for determining whether an application is ready for the Marketplace.

This article is part of a series focused on creating and publishing well-designed AI applications and agents on Microsoft Marketplace. 

AI applications and agents that demonstrate quality—via comprehensive evaluation frameworks, clearly defined release criteria, and ongoing measurement—are simpler to assess, trust, and implement. Providing clear evidence of quality can ease the Marketplace review process, set clear expectations during customer onboarding, and cultivate long-term trust in actual use. When quality is both visible and measurable, conversations shift from “Does this work?” to “How can we scale it?”—a position publishers aspire to achieve.

Publishers who prioritise quality as a key discipline lay the groundwork for safe innovation, customer loyalty, and sustainable growth through Microsoft Marketplace. This foundation arises from the decisions, frameworks, and evaluation practices established well before any solution reaches the review stage.

Quality in AI applications and agents isn’t defined by a single metric—it’s about interconnected dimensions that collectively determine whether a system fulfills its intended purpose for its users. The HAX Design Library—Microsoft’s resource for human-AI interaction design patterns—provides practical guidance for each of these aspects. It’s essential to define these dimensions before any evaluation can commence; you can only measure what you’ve clearly described.

• Accuracy and relevance — Does the output provide the correct answer within the right context? HAX patterns such as defining system capabilities (G1) and alerting users about AI uncertainty (G10) aid publishers in creating systems where accuracy is noticeable and outputs are understood in the correct context—rather than being viewed as always authoritative.
• Safety and alignment — Does the output remain within intended use, avoiding harmful, biased, or policy-violating content? HAX patterns that address social biases (G6) and support efficient correction (G9) ensure outputs remain within acceptable limits, allowing users to identify and rectify issues proactively.
• Consistency and reliability — Does the system operate predictably across different users, sessions, and environments? HAX patterns that recall recent interactions (G12) and inform users about changes (G18) help maintain coherent behaviour during sessions, ensuring that updates to models or prompts are communicated clearly.
• Fitness for purpose — Does the system perform its intended role for the target audience under real-world conditions? HAX patterns clarify how effectively the system can achieve its goals (G2) and react to user context and needs (G4), ensuring responses align with what users actually require—not just what they have entered literally.

These dimensions work in concert; deficiencies in any area are likely to manifest in live environments, often in ways that are challenging to trace without a well-designed evaluation framework.

It’s crucial to construct evaluation frameworks alongside the solution itself. Identifying gaps post-development is much harder and costlier. This disciplined approach mirrors early design considerations related to security and governance: the choices made upfront influence what can be measured, improved, and prepared for release.

A robust evaluation framework outlines five key areas:

1. What to measure — This pertains to the quality dimensions most relevant for your solution and its intended use cases. For AI applications and agents, this generally includes task adherence, response coherence, groundedness, safety, and fitness for purpose.
2. How to measure — This covers the tools, methods, and benchmarks used for consistent quality assessment. Effective evaluations include AI-assisted evaluators (which leverage models to assess outputs), rule-based evaluators (which apply deterministic logic), and human reviews for complex scenarios and safety-related responses that automation can’t fully address.
3. Who evaluates — A combination of automated metrics, human assessment, and structured customer feedback is key. No single method is sufficient; the framework should clarify how to apply each and when to prioritise human judgment.
4. When to evaluate — Defined milestones are important: during development to establish a baseline; pre-release to validate against acceptance criteria; during rollout to capture regressions; and continuously in production to monitor changes as models, prompts, and data evolve.
5. What triggers re-evaluation — Events like model updates, prompt adjustments, new data sources, tool integrations, or significant shifts in customer usage patterns should prompt re-evaluation. This should be a scheduled process, not a reactive measure to visible failures.

The framework serves as a shared resource—helping both the publisher in releasing safely and customers in understanding the quality commitments they agree to when deploying a solution in their environment.

Evaluate your AI agents – Microsoft Foundry | Microsoft Learn

Quality should be evaluated through complementary approaches, each designed to unveil a different kind of risk at varied phases of the solution’s lifecycle.

• Automated metric evaluation — Evaluators appraise agent responses against predefined criteria at scale. Some use AI models to score outputs based on task adherence, coherence, and groundedness; others may employ deterministic rules or text similarity algorithms. Automated evaluation is most effective when acceptance thresholds are established in advance—for instance, requiring a minimum task adherence pass rate before allowing a release to move forward.
• Safety evaluation — This dedicated evaluation category seeks to identify potential risks in content, such as policy violations or harmful outputs. Safety assessments should run concurrently with quality evaluations rather than as an afterthought.
• Human-in-the-loop evaluation — This involves structured expert reviews of edge cases, borderline outputs, and safety-critical responses where automated metrics may fall short. Human judgment is vital for understanding context, intent, and impact.
• Red-teaming and adversarial testing — This involves challenging the system with unexpected or intentionally misused inputs (like prompt injection attempts) to reveal potential failure points before they affect customers. Microsoft offers dedicated AI red teaming guidance specifically for agent-based systems.
• Customer feedback loops — Collecting structured signals from users interacting with the system in a live setting is crucial. Real-world feedback helps bridge the divide between what was tested and how customers actually utilise the product.

Each evaluation method plays a unique role. The framework specifies when and how to employ each of these approaches, and which results are necessary before approving a release, accepting a change, or expanding a capability.

Quality evaluation fosters improvement only when linked to clear release criteria. In an LLMOps model, these criteria are automated checkpoints integrated directly into the CI/CD pipeline, uniformly applied at every stage of the release process.

In continuous integration (CI), automated evaluations occur with every modification—whether updating a prompt, advancing to a new model version, or altering a data source. CI gates help identify regressions early, prior to reaching customers, by validating outputs against pre-established quality thresholds for task adherence, coherence, groundedness, and safety.

In continuous deployment (CD), quality gates assess whether a build can progress. Release criteria should outline:

• Minimum acceptable thresholds for each quality dimension—releases cannot proceed unless these thresholds are met.
• Known failure modes that prevent release versus those that are trackable, monitored, and accepted within defined risk limits.
• Deployment constraints—conditions under which a release might be paused, rolled back, or gradually introduced to a select group of users before full deployment.

Regular evaluations must be both scheduled and adaptive. As models, prompts, tools, and patterns of customer interactions evolve, the baseline also shifts. LLMOps considers re-evaluation a continuous practice: conduct evaluations, pinpoint vulnerabilities, make adjustments, and re-evaluate prior to implementing changes.

This disciplined approach ties directly to governance. Quality evidence—the documented record of what was measured, when, and using what criteria—forms part of an audit trail that ensures AI behaviour is accountable, understandable, and trustworthy over time. For more on the governance foundation this is built upon, see “Governing AI apps and agents for Marketplace readiness.”

Clearly defined quality ownership minimizes friction during onboarding, fosters confidence in operation, and protects both parties if behaviours change. Within the context of the Marketplace, quality is a shared responsibility, yet the boundaries of that responsibility are well-defined.

Publishers are responsible for:

• Designing and implementing the evaluation framework throughout development and release.
• Clearly defining quality dimensions and thresholds that align with the solution’s intended use.
• Providing customers with transparency regarding what quality means for their solution, while safeguarding proprietary prompts or internal logic.

Customers are responsible for:

• Verifying that the solution operates correctly in their specific environment, with their data and users.
• Setting up feedback and monitoring systems to capture quality signals in their environment.
• Maintaining quality evaluation as a shared, ongoing responsibility rather than a one-off guarantee from the publisher.

When both publishers and customers understand their roles, quality transforms from a mere handoff to a foundational element—one that promotes adoption, nurtures trust, and equips both parties to respond confidently when behaviours change.

A strong quality framework establishes the baseline, but maintaining that quality as solutions grow is a separate challenge. Upcoming articles in this series will delve into aspects beyond the framework: API resilience, performance enhancement, and operational monitoring for AI applications and agents in live settings.

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