AB-100 Agent Evaluation: From Test Cases to Telemetry

AB-100 Agent Evaluation: From Test Cases to Telemetry

An AI agent correctly summarizes a customer conversation and receives a strong language-quality score. But the same run creates a duplicate case, cites an expired policy and tells the customer a refund is approved before the finance system commits it. Evaluating the model alone would miss the failures that matter to the business.

The AB-100 exam puts substantial weight on deploying business solutions, including agent tests, telemetry, feedback, tuning and responsible operation. Solution architects need release criteria for the entire workflow: user intent, retrieval, tools, decision, business state and the human escalation path.

Write acceptance criteria from the business outcome

For a service agent, success is not a polite answer or a conversation that ended without escalation. Define acceptable case resolution, correct record selection, policy compliance, verified transactions, safe refusals and understandable handoffs. A one-minute answer that produces a wrong refund is worse than a longer conversation that correctly routes the customer to a specialist.

Involve the support director and owners of transactional systems in defining acceptance rules. Technical engineers can specify latency and service errors; security reviewers can specify access boundaries; the business owner must say what a correct operational decision means. A single aggregated quality score cannot replace those different requirements.

Record outcomes under stable scenario identifiers, including model and agent versions, source-data revision and external tool response. Tests should be reproducible and tied to approved expected behavior rather than the style preferred by one reviewer.

Create a representative test matrix before tuning

Include typical inquiries, ambiguous customer identities, expired return windows, regional policy differences, conflicting knowledge sources and an unavailable order API. Add negative cases: attempts to read another customer’s data, instructions embedded in retrieved documents that try to redirect agent behavior, and requests for actions the current user cannot authorize.

Sampling only successful conversations will make almost any agent appear reliable. Choose cases based on actual operational frequency and business consequence. Rare but high-impact failures, such as issuing an unauthorized financial adjustment, deserve separate acceptance gates even if they barely affect an average response-quality score.

Keep the test dataset within permitted privacy boundaries. Synthetic customers and controlled mock transactions are often more suitable for repeated evaluation than live support cases. If actual data is needed under policy, remove unnecessary fields and govern its retention and access.

Evaluate retrieval, reasoning and action independently

Imagine two runs of a fictional replacement-parts agent. The first retrieves the correct product manual but chooses an incompatible spare part; the second chooses the correct part but the inventory API times out and the agent tells the user it has reserved stock. Both runs may produce fluent text and even reasonable citations, yet fail different acceptance criteria. Give each stage a separate observable outcome: source selection, constraint interpretation, tool inputs, committed result and truthful user message. If the problem is a stale inventory record, changing the model may not help. If the tool committed a reservation without returning an acknowledgment, the system needs reconciliation rather than a blind retry. These distinctions make evaluation useful to both engineers and the business owner.

A wrong response may originate in a stale SharePoint source, a failed Dataverse join, an incorrect model interpretation or a tool returning a misleading success code. If every error is labeled hallucination, teams can waste effort changing models when the underlying record association is broken.

Test retrieval against specific source versions and access entitlements. Evaluate whether the final answer is supported, but also inspect whether the correct API was called with the correct authorized fields. Business actions need postconditions: the case was updated once, the refund status exists in the system of record and the audit trail identifies the request.

The Foundry models and evaluation guide supplies related platform context. The AB-100 release decision extends beyond a model benchmark to business workflow behavior under failure.

Use multiple metrics without hiding their limitations

Review metric disagreements rather than averaging them away. A model-assisted evaluator may praise an answer that follows the supplied passage, while a business reviewer can identify that the passage comes from an expired warranty policy. Similarly, tool success may be reported by a middleware service even when the downstream transaction is pending. Capture the source and meaning of each metric and define which evidence takes precedence for consequential operations. A useful scorecard tells an operator what failed and who must respond, not merely whether an overall number exceeded a threshold.

Task completion measures whether the intended supported outcome occurred. Groundedness evaluates whether a response follows provided evidence, but a grounded answer can still cite obsolete information. Tool success is useful only if the tool actually committed the desired business state. Safety checks should include unauthorized disclosure, inappropriate action requests and behavior when evidence is missing.

Track latency per step, total conversation cost, escalation rates, recontacts and customer satisfaction where reliably measurable. A faster agent may consume more requests through retries, while a high deflection rate can reflect prematurely closed cases. Compare each metric with actual downstream service outcomes.

When using automated judges or rubrics, calibrate them with human-reviewed examples and investigate disagreement. A generated score is another model output with limitations, not unquestionable proof of compliance.

Make telemetry useful without turning it into a data leak

Trace the path from customer intent to retrieval and tool invocations with correlation identifiers, timestamps and component versions. Operators should see whether the order lookup failed, which policy version was consulted and what result the transaction service returned. Without that detail, a generic “the agent answered incorrectly” ticket rarely leads to a fix.

Collect the minimum business information needed for debugging and audit. A platform performance engineer may need error rates and latency rather than full customer transcripts. A case investigator may require authorized access to a particular action record, with retention and privacy controls. Distinguish each need before sending large amounts of personal content into common telemetry stores.

For multi-agent systems, capture which component made a decision and which performed a write. A coordinator reporting success does not confirm that the downstream tool completed its transaction.

Monitor model drift and the surrounding system together

Agent behavior can change when a model deployment updates, a prompt is revised, a tool schema changes or a knowledge index ingests a new policy. A sudden increase in unresolved cases may come from stale data rather than a change in model quality. Keep versions and release times alongside incident trends.

Use a production health view that combines service availability, tool errors, retrieval freshness, latency, quality-review samples and business outcomes. Monitor changes by cohort where useful: region, channel, language or case category. A global average can conceal a serious defect in one smaller but important workflow.

Establish alert thresholds with the team that operates the system. A meaningful alert should guide someone toward a diagnosis and an approved response. Repeated noisy warnings are not evidence of strong observability.

Test human approvals and refusal paths explicitly

An agent can prepare a draft credit recommendation, but an authorized manager may need to approve the actual financial action. Test the approval gate with a permitted request, a request above the policy threshold, a missing customer identity and an unavailable approver. The expected output should make the state clear: requested, pending, rejected or committed.

If a human declines approval, the agent must not retry the action under a different tool or ask another specialist to circumvent the decision. The action service should enforce authorization independent of conversational language. Validate that audit records show the actual approving principal and that users are told the truthful business status.

A good refusal protects the customer and leaves a usable route to resolution. Measure unnecessary refusals separately from unsafe approvals; both can damage trust but need different remedies.

Stage changes and keep a tested rollback

A change to model selection, tool permissions, retrieval configuration or agent instructions can affect existing behavior. Compare candidate and approved versions on the same test ledger, then test integrations in a controlled environment before exposing them to customers. Small staged releases can reveal defects that a test lab missed, but they should have measurable stop conditions.

Maintain a route to disable a dangerous action or restore an approved version if error rates rise. Rolling back model instructions alone may not repair an incompatible connector schema or new policy index. Record dependency versions and the business state created during the faulty release so reconciliation can occur.

Retest cases that previously failed; otherwise a superficially successful fix might recreate an earlier defect. A release checklist should cover source data, model behavior, tool authorization and transaction postconditions, not only deployment status.

Use an incident review to improve evaluation design

Suppose a customer is told a credit was applied, but no finance transaction exists. Reconstruct the trace to find whether the tool failed, returned an ambiguous response or was never invoked. Update the test set with that exact failure mode and a clear expected status message. Assign ownership for the connector correction and any customer remediation.

Now test a second case where the transaction committed but the acknowledgment timed out. Without idempotency, a retry can create duplicate credits. The accepted solution should identify the existing transaction and return truthful status rather than blindly retrying. This is a business-level reliability property, not a stylistic response improvement.

AB-100 readiness means the architect can explain how the enterprise will detect, measure, prevent and recover from such behavior after the pilot team stops watching every conversation.

• Category: Certifications Microsoft