Agent Evaluation.

Agent evaluation is the process of testing and reviewing an AI agent’s performance against defined expectations. Those expectations may cover the quality of its responses, its use of connected tools or knowledge, its ability to complete a task, and the way it handles uncertainty or requests outside its scope.

In a Microsoft support context, the term can describe how a team assesses an agent that answers support questions, assists staff, or participates in a service workflow. It is a general practice, not the name of one Microsoft product feature. Evaluation methods and available evidence vary by platform, configuration, connected systems, and the agent’s intended role.

Evaluation is most useful when it informs a decision: whether to revise the agent, limit its scope, add human review, or proceed with a controlled release. Testing cannot prove that an agent will behave correctly in every future interaction.

A score can summarize results, but it does not explain what the agent did well or where it failed. A high average may conceal a serious weakness in a less common but consequential case. A lower score may reflect a strict rubric or a small test set rather than a broad performance problem.

Good evaluation connects each result to a task, a criterion, and evidence. For example, “the agent answered incorrectly” is less actionable than “the agent used an outdated troubleshooting procedure when the request described a newer configuration.” The second finding points toward a specific cause to investigate, though it still needs verification.

A test set should reflect the work the agent is expected to do, not merely the easiest requests to answer. Include typical cases as well as variations that reveal how the agent handles missing details, ambiguous intent, conflicting information, and requests beyond its role.

When assembling cases, teams can draw from several kinds of examples:

• Common support questions that represent routine work.
• Requests with incomplete or unclear details, where clarification may be the right response.
• Cases requiring the agent to use an approved knowledge source or connected action.
• Out-of-scope requests that should lead to a refusal, limitation, or human handoff.
• Edge cases involving contradictory information, failed integrations, or changed conditions.

For each case, record what a suitable outcome looks like and why. An expected answer may be a range of acceptable responses rather than one exact sentence. That distinction helps avoid penalizing useful wording differences while still identifying factual or procedural errors. Test material should also be reviewed for sensitive information and kept appropriate to its intended use.

Different tasks call for different criteria. An agent that summarizes a case should not necessarily be judged by the same rubric as one that recommends troubleshooting steps or initiates a workflow.

A balanced assessment may examine:

• Accuracy: Are factual statements and procedural guidance supported by appropriate information?
• Relevance: Does the response address the user’s actual request?
• Completeness: Does it include the details needed for the task without adding unsupported claims?
• Uncertainty handling: Does it ask for clarification, qualify an uncertain answer, or hand off when appropriate?
• Tool and source use: Were connected actions and knowledge sources used appropriately, and were their results represented accurately?
• Safety and scope: Did the agent respect its permissions and stay within its intended role?
• Outcome quality: Was the task completed, or did the response create avoidable rework or confusion?

The criteria should be observable enough that reviewers can apply them consistently. If two reviewers interpret a rubric differently, the evaluation process may need clearer definitions or examples before the results are used to make deployment decisions.

Consider an agent intended to help support staff prepare a case summary from a user’s description and relevant troubleshooting notes. The team tests routine reports, reports with missing details, and cases where the notes contain conflicting information.

In one test, the agent produces a concise summary but presents an unconfirmed cause as established fact. A simple completeness score might miss the problem. A criterion for distinguishing confirmed information from hypotheses would surface it. The team could then revise the agent’s instructions or review process and test the same case again, along with other cases that involve uncertainty.

This example also shows why the evaluation target must be explicit. If the agent is intended only to prepare a draft, reviewers should assess the draft and the review handoff, not assume that the agent independently diagnosed or resolved the issue.

Evaluation should continue as the agent, its instructions, its connected information, or its operating environment changes. A practical cycle can be:

1. State the decision. Define what the team needs to learn, such as whether a change improved summaries or whether a workflow is ready for a limited release.
2. Select representative cases. Use relevant test examples, including cases that previously exposed errors.
3. Apply clear criteria. Decide how reviewers will judge correctness, task completion, uncertainty handling, and other relevant qualities.
4. Run the agent under defined conditions. Record the configuration and dependencies that materially affect the test so results can be interpreted.
5. Review evidence and failures. Examine individual cases, not just aggregate scores. Identify patterns and distinguish confirmed causes from hypotheses.
6. Make a controlled change. Adjust the instructions, knowledge, workflow, permissions, or scope as appropriate.
7. Retest and compare. Check whether the change addressed the original issue without causing regressions elsewhere.

For higher-impact workflows, automated checks may help with repeatable measurements, while human review can assess nuance, usefulness, and context. The balance depends on the task and the consequences of an incorrect result.

Evaluation results are limited by the cases tested and the conditions under which they were run. A carefully designed test set can still omit real-world situations. Results may also vary when the agent’s configuration, connected services, or source content changes. A passing result is evidence about the tested conditions, not a blanket guarantee.

Before using findings to approve or expand an agent, teams should consider:

• Whether the test cases reflect the intended users and actual support tasks.
• Whether important failures are obscured by averages or broad pass rates.
• Whether the evaluation was repeated after relevant changes.
• Whether reviewers can explain why a result passed or failed.
• Whether the cost of a failure warrants additional controls or human review.

Evaluation should not be treated as a substitute for operational monitoring, security review, or governance. Those practices answer related but different questions. For example, evaluation tests whether an agent handles defined cases as intended, while operational monitoring helps detect issues during use.

An evaluation program is useful when its findings lead to decisions that can be explained and revisited. Teams can use results to refine the agent’s scope, improve test coverage, change a workflow, or decide that a task needs human ownership. Keeping a record of criteria, test cases, configuration, findings, and follow-up actions makes later comparisons more meaningful.

Confidence should grow from repeated evidence across relevant tasks, not from a single score or demonstration. For support teams, the practical goal is an agent whose behavior has been examined against its intended responsibilities, whose limitations are understood, and whose performance can be reassessed when conditions change.