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The right AI-agent testing stack is a workflow, not a single product. Start with deterministic, in-memory checks for routing, tool orchestration, guardrails, retries, streaming, and session logic that your application owns. Add traces and trace graders to diagnose real runs, then turn representative failures into datasets and repeatable evaluation runs. Use simulated multi-turn conversations before deployment and real conversations or traces after launch. Choose a platform only after checking evaluator scope, framework support, hosting, retention, and regional constraints.
What AI-agent testing actually covers
An agent can fail in several different places: your routing code can select the wrong specialist, a model can choose an unsafe or irrelevant tool, a handoff can lose context, a guardrail can miss a policy violation, or a long conversation can drift even though each individual answer looks acceptable. A useful testing program separates those failure modes instead of assigning every problem to an LLM judge.
- Application and SDK behavior: deterministic checks for orchestration, tool dispatch, handoffs, guardrails, retries, streaming, sessions, and event handling.
- Provider and model behavior: integration tests that exercise the actual model, retrieval service, sandbox, speech system, or other external dependency.
- Workflow behavior: trace inspection and grading across a complete run, including tool choice, arguments, handoffs, policy adherence, and task outcome.
- Regression behavior: datasets and repeatable eval runs that let you compare prompts, routing changes, model versions, and code releases over time.
- Production behavior: monitoring of real conversations and traces, with retention and data-handling rules that match your organization.
These layers answer different questions. A local test can prove that your retry counter stops after three attempts; it cannot prove that a model will choose the correct tool for an unfamiliar request. A trace can show why one run failed; a dataset and eval run can show whether a new prompt improves the failure rate across many cases.
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Choose tools by the failure you need to find
| Primary concern | Best first method | What it can establish | What it cannot establish alone |
|---|---|---|---|
| Incorrect routing, handoff, retry, or guardrail code | Deterministic local tests | Whether known inputs produce the expected application events and state transitions | How an external model behaves on novel language |
| Wrong tool, malformed arguments, or policy violation in a run | Traces plus trace grading | Which model calls, tools, handoffs, and guardrails occurred and whether they met a rubric | Statistical improvement across future releases without a repeatable dataset |
| Prompt or model regression | Curated dataset and repeated eval runs | Comparable scores and failure examples across versions | Perfect truth; judge scores still depend on rubric and evaluator quality |
| Unreleased multi-turn workflow | Simulated conversations | Controlled coverage of goals, turns, interruptions, and edge cases before deployment | All of the variation and distribution of real users |
| Post-deployment quality and safety | Existing conversations or traces with monitoring | How the system behaves with actual traffic and where failures cluster | Data that was never captured or retained |
Do not rank products by a single score. The documentation for the tools below does not provide a comparable price, latency, accuracy, or feature-limit matrix, so those dimensions are intentionally not used as buying criteria here.
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Deterministic testing for code your team owns
OpenAI Agents SDK testing utilities
The OpenAI Agents SDK documents testing utilities that run in memory, are deterministic, and make no model, sandbox-provider, or Realtime API requests. They are designed for the interaction records and orchestration that the SDK and your application control. You can test tool execution, handoffs, guardrails, retries, streaming, session behavior, Sandbox capabilities, Realtime events, and voice-pipeline composition without paying for or depending on a live provider call.
That isolation makes these tests suitable for the inner development loop and continuous-integration smoke checks. Build fixtures for known user intents, then assert the sequence of events rather than only the final text. For example, a refund request might be required to route to a billing specialist, call an order lookup tool once, and refuse a refund when the policy guardrail reports an ineligible order. A streaming test can assert that the stream closes and that the final event contains the expected handoff. A retry test can inject a controlled tool failure and verify the configured retry limit and fallback path.
These utilities do not test behavior owned by an external model or provider. If the question is whether a particular model recognizes an ambiguous request, whether a hosted retrieval index returns the right document, or whether a sandbox provider handles a real command, use a real adapter or an integration environment. Keep those slower and less deterministic checks separate from the fast local suite so a provider outage does not look like an application regression.
Microsoft Agent Framework local checks
Microsoft Agent Framework documents a LocalEvaluator for checks that run without API calls. Its documented examples include keyword checks and tool-called checks. These are useful for inner-loop development, CI smoke tests, and fast iteration when the expected condition is explicit: a response must contain a required term, or a particular tool must have been called.
Local checks should be narrow and diagnostic. A keyword assertion can catch a missing confirmation phrase, but it is not a substitute for a quality or safety evaluation. A tool-called assertion can verify that a route reached the calculator, but it does not prove that the arguments were correct or that the final answer was grounded. Pair local checks with integration and judge-based evaluations where the behavior is inherently probabilistic.
How to structure a local test
- Define the event contract. List the expected route, tool calls, arguments, handoffs, guardrail decisions, retries, and terminal state for a scenario.
- Stub external boundaries. Replace model, retrieval, sandbox, speech, and network adapters with deterministic fakes when you are testing application logic.
- Inject failures deliberately. Return a tool error, timeout, malformed result, or blocked input and assert the recovery branch.
- Assert events and state. Check structured records, not only the wording of the final response.
- Run on every change. Keep the suite fast enough for pull requests and CI; move provider-backed checks to a separate integration job.
Tracing: see what happened during an agent run
A trace is an end-to-end record of a run. OpenAI’s tracing documentation describes recording model generations, tool calls, handoffs, guardrails, and custom events. A trace lets an engineer reconstruct the path from the user request to the final response instead of inferring the cause from text alone.
Use traces diagnostically. When a task fails, inspect whether the model selected the wrong tool, supplied invalid arguments, skipped a required handoff, violated an instruction, or received an unexpected tool result. Compare successful and failed traces for the same scenario. Add custom events for business-level milestones such as “identity verified,” “refund policy checked,” or “approval required,” because those events make a workflow understandable to reviewers who do not need every token-level detail.
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The OpenAI SDK documentation says tracing is enabled by default and also documents ways to disable it. It identifies an important deployment constraint: tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy. Confirm current retention, regional processing, access-control, and redaction requirements before enabling traces for sensitive workloads.
Trace grading: score workflow behavior, not just prose
Trace grading applies evaluators to recorded runs. The OpenAI evaluation guidance positions it for questions such as whether the agent selected the right tool, made an appropriate handoff, followed an instruction or safety policy, and how a prompt or routing change affected end-to-end behavior.
A useful trace grader reads structured events and applies a clear criterion. Examples include:
- Tool selection: Was the inventory lookup used instead of a general web search?
- Argument validity: Did the order identifier match the required format before the tool call?
- Handoff correctness: Did a security-sensitive request move to the identity-verification agent?
- Policy adherence: Did the agent refuse an unauthorized action and explain the next safe step?
- Outcome: Did the workflow reach the required business state, not merely produce fluent text?
Store the grader’s decision and the evidence it used. A score without the relevant trace span is difficult to debug and easy to mistrust. Review borderline cases manually, revise the rubric when reviewers disagree, and keep a small set of adjudicated examples as references.
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Datasets and repeatable evaluation runs
Selected traces are excellent for discovering failures, but they are not a stable benchmark. Once you know what “good” means, convert representative requests, expected outcomes, tool requirements, and safety cases into a dataset. The OpenAI Platform guidance describes datasets and repeated eval runs as the path for benchmarking changes, comparing prompts, and running larger evaluations over time.
Build a useful dataset
- Include ordinary successful requests, not only dramatic failures.
- Keep every recurring failure pattern, with the original input and the observed error.
- Record expected tool, handoff, or policy outcome where one is known.
- Add adversarial and ambiguous cases that exercise refusal, clarification, and fallback behavior.
- Version the dataset when requirements change; do not silently replace old cases.
- Separate development examples from a held-out set used for release decisions.
Run a regression comparison
- Freeze the dataset version, model configuration, tools, and evaluator rubric.
- Run the current production candidate and the proposed change under the same conditions.
- Compare task completion, tool choice, safety, groundedness or response quality, and cost or latency only when your own measurements are available.
- Inspect regressions by scenario category rather than relying on one aggregate score.
- Review judge disagreements and a sample of passes before approving the change.
An aggregate improvement can conceal a serious safety regression in a small category. Set release gates for critical criteria, such as zero unauthorized account changes, in addition to an overall quality target.
Simulating multi-turn conversations before deployment
Microsoft Foundry documents both full-conversation and individual-turn evaluation. It supports simulated data for controlled pre-deployment scenarios and existing conversations or traces for evaluating previous interactions. Full-conversation evaluation is labeled preview in that documentation, so verify its current availability and behavior in the environment you plan to use.
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When to use full conversations
Use a simulated full conversation when success depends on context carried across turns: authentication followed by an account change, a clarification that changes the requested product, a user correction after a tool error, or a handoff between specialists. Define the user goal, allowed actions, expected state transitions, and termination condition. Vary phrasing, turn order, interruptions, and incomplete information while keeping the business objective constant.
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Turn-level evaluation is appropriate for a focused response or tool decision where earlier context is fixed or irrelevant. It is faster to diagnose and can provide dense coverage for classification, extraction, safety refusal, or argument formatting. Do not infer that passing turn-level checks proves that the agent will maintain context over ten turns.
Move from simulation to real traffic
Simulated scenarios provide controlled coverage; they cannot represent every user strategy, domain term, or unexpected interruption. After deployment, evaluate existing conversations and traces where policy permits. Sample by workflow, risk, language, and outcome so monitoring does not overrepresent easy requests.
Design evaluators around a rubric
Microsoft’s agent-evaluation guidance recommends a rubric evaluator as the primary measure, supplemented with task, quality, text-similarity, safety, or custom evaluators as needed. A rubric should describe observable criteria and scoring anchors rather than asking a judge whether an answer “seems good.” Review generated rubrics instead of treating a judge score as ground truth.
Example rubric dimensions
| Dimension | Pass condition | Common evidence |
|---|---|---|
| Task completion | The requested business objective is completed or a valid next step is provided | Final state, confirmation, or explicit clarification request |
| Tool use | Required tools are selected with valid arguments and no prohibited tools are used | Trace spans and tool payloads |
| Groundedness | Claims are supported by retrieved or supplied information | Citations, retrieved passages, or structured records |
| Conversation quality | The response is clear, relevant, and appropriately concise for the task | Turn text and conversation context |
| Safety | Disallowed actions are refused and safe alternatives are offered | Guardrail events and final response |
| Recovery | Errors, missing data, and ambiguity trigger the documented fallback | Error events, retries, and handoff state |
Use deterministic evaluators whenever the criterion is exact: required keyword, tool-called event, schema validity, or forbidden action. Use an LLM judge for qualities that require interpretation, such as relevance or grounded explanation, and calibrate it against human-reviewed examples. A similarity score can detect wording or semantic overlap, but it should not be the sole measure of task success.
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| Option | Local or provider-backed | Trace and evaluation model | Conversation and data modes | Notable constraints |
|---|---|---|---|---|
| OpenAI Agents SDK testing utilities | Deterministic, in-memory, provider-neutral for SDK-owned records | Application assertions; pair with platform traces and trace grading for workflow behavior | Interaction records, sessions, streaming, handoffs, and related SDK events | Does not make model, sandbox-provider, or Realtime API requests; external behavior needs real adapters or integration tests |
| OpenAI tracing and evaluation | Provider-backed traces and graders | End-to-end traces, trace grading, datasets, and repeated eval runs | Selected traces first, then curated datasets for regression | Tracing is unavailable for organizations using OpenAI APIs under Zero Data Retention |
Microsoft Agent Framework LocalEvaluator |
Local checks without API calls | Keyword and tool-called checks; combine with cloud evaluators for broader criteria | Best for inner-loop and CI checks | Exact checks do not establish probabilistic quality or external-provider behavior |
| Microsoft Foundry evaluations | Cloud LLM-as-judge and other evaluators | Rubric, task, quality, text-similarity, safety, and custom evaluators | Individual turns, full conversations, simulated data, existing conversations, datasets, and traces | Full-conversation evaluation is documented as preview; review generated rubrics |
| Arize Phoenix | Deterministic code evaluators and LLM-as-judge evaluators | Evaluator runs traced through OpenTelemetry and a trace viewer for decisions | Supports tool-calling-agent evaluation; production monitoring is directed to Arize AX | Confirm hosting, retention, and regional terms for your deployment |
The table describes documented testing styles, not a universal ranking. Select the option that matches your existing framework, telemetry format, deployment boundary, and evaluator requirements.
A practical implementation workflow
1. Inventory boundaries and risks
Draw the agent’s path from input to outcome. Mark code you own, external model or provider calls, tools that change data, sensitive fields, and irreversible actions. Assign a risk level to each workflow so high-impact paths receive stricter gates and human review.
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2. Establish the local contract suite
Write deterministic tests for routing, tool schemas, handoffs, guardrails, retries, streaming completion, session persistence, and error recovery. Use in-memory SDK utilities or local framework checks where they fit. Keep provider calls out of this suite unless the behavior under test belongs to that provider.
3. Instrument traces with useful events
Capture model generations, tool calls, handoffs, guardrails, and custom business events. Redact secrets and personal data according to your policy. Define who can view traces and how long they are retained. If your organization uses a Zero Data Retention policy with OpenAI APIs, account for the documented tracing limitation rather than assuming traces will be available.
4. Grade representative runs
Select successful, failed, ambiguous, and adversarial traces. Apply graders for tool choice, arguments, handoffs, policy adherence, task completion, and response quality. Save evidence with each decision and have reviewers adjudicate disagreements.
5. Create and version a dataset
Promote recurring failures and important successes into a curated dataset. Label expected outcomes and critical safety rules. Keep a held-out set for release comparisons, and record the model, prompt, tool definitions, evaluator versions, and dataset version for each run.
6. Simulate multi-turn paths
Generate controlled conversations for authentication, clarification, interruption, tool failure, handoff, and recovery cases. Evaluate both individual turns and the full conversation when the product supports it. Mark preview features and validate them before making them a release gate.
7. Gate releases and monitor production
Set hard gates for critical safety and authorization failures, plus thresholds for task completion and quality. After release, sample real conversations or traces, investigate clusters, and feed confirmed failures back into the dataset. Keep monitoring separate from pre-deployment simulation so each serves its purpose.
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Metrics that help you make a decision
Choose metrics that map to the workflow’s real risk:
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- Task completion rate: whether the business objective was achieved, deferred correctly, or failed.
- Tool-selection accuracy: whether the required tool was chosen and prohibited tools were avoided.
- Argument validity: whether calls met schemas and contained correct identifiers or constraints.
- Handoff accuracy: whether the request reached the appropriate specialist with context intact.
- Safety and authorization failure rate: unauthorized actions, unsafe disclosures, or missed refusals.
- Recovery rate: whether the agent handled tool errors, timeouts, ambiguity, and partial data as designed.
- Groundedness and quality: whether claims are supported and responses meet the rubric.
- Operational measures: latency, token use, provider errors, and trace completeness, measured in your own environment.
Report confidence and sample size for trend decisions when you have enough observations. A small improvement on a tiny dataset is a hypothesis, not proof. Segment results by workflow, language, risk class, and model configuration so an easy category does not mask a failing one.
Common mistakes and how to correct them
Using a judge for every question
If a required tool call or schema is exact, use a deterministic assertion. Reserve an LLM judge for interpretation and review its rubric and examples.
Testing only the final response
Fluent text can hide a wrong tool, invalid argument, skipped guardrail, or lost handoff. Grade the trace and structured state as well as the prose.
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Simulation controls variables but cannot reproduce every real user. Add existing conversations or traces after deployment where privacy and retention rules allow.
Mixing local and integration failures
A provider outage should not fail a unit suite, and a passing stub should not certify a live retrieval or model behavior. Maintain separate jobs and label their guarantees.
Changing the rubric without versioning it
Scores are not comparable when criteria change silently. Version rubrics, evaluators, datasets, prompts, and model settings together.
Ignoring hosting and retention requirements
Trace data may contain user content, tool arguments, and sensitive identifiers. Verify regional processing, retention, access, redaction, and zero-retention compatibility for every system before collecting production traces.
Choosing by a feature checklist alone
A tool that supports many evaluator types may still be a poor fit if it cannot ingest your traces, run in your required region, or integrate with your framework. Pilot the smallest workflow that exercises your highest-risk failure mode, then expand.
A compact decision framework
- If your immediate problem is orchestration code, begin with deterministic in-memory or local checks.
- If you cannot explain failed runs, add end-to-end tracing and inspect tool, handoff, guardrail, and custom events.
- If you need to compare changes, grade representative traces, then build a versioned dataset and repeatable eval run.
- If context across turns matters, simulate full conversations before deployment and evaluate individual turns for focused coverage.
- If the agent is live, sample existing conversations or traces and monitor by risk category.
- Before committing, verify framework and provider integration, evaluator types, hosting, retention, regional handling, and any preview or zero-retention limitations.
No documented option is universally best. The strongest setup combines a fast deterministic contract suite, trace-based diagnosis, rubric-driven graders, curated regression data, controlled simulation, and privacy-aware production monitoring.
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