The right AI-agent testing stack is a workflow, not a single product. Start with deterministic, in-memory checks for routing and tool orchestration your team controls. Add traces and graders to diagnose real runs, then turn representative failures into datasets and repeatable evaluation runs. Use simulated multi-turn conversations before deployment and existing conversations after launch. The choice among OpenAI Agents SDK and Platform, Microsoft Agent Framework and Foundry, and Arize Phoenix depends on those jobs, your evaluator data, and your hosting and retention requirements—not on an unsupported universal leaderboard.
What AI-agent testing actually covers
An agent is a chain of decisions rather than a single text response. It may select a tool, pass arguments, hand work to another agent, apply a guardrail, retry after an error, maintain session state, stream partial output, and combine results into a final answer. A useful test program therefore checks both the parts your application owns and the behavior supplied by an external model or service.
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Those two categories need different evidence. A local test can prove that your router sends a request to the correct tool under a known condition. It cannot prove that a hosted model will always interpret an ambiguous request correctly. Conversely, a production trace can reveal a poor tool choice, but it is a slow and noisy way to catch a typo in your handoff code. Treat testing as a set of layers with different speed, cost, and realism.
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Classify the failure before choosing a tool
Write down the failure you need to prevent in plain language. The wording usually points to the appropriate test layer.
| Failure or question | Best first layer | Evidence to collect |
|---|---|---|
| Did the application route a request to the intended specialist? | Deterministic local check | Expected handoff or route and the observed route |
| Did a tool receive the required arguments and run once? | Deterministic local check, then an integration test for the real adapter | Tool name, arguments, call count, and returned error |
| Did a guardrail block unsafe input? | Local check for the guardrail logic, plus provider-backed scenarios for model-dependent interpretation | Guardrail decision, reason, and user-visible result |
| Why did one live run choose the wrong tool? | Trace inspection and trace grading | Model calls, tool calls, instructions, handoffs, and guardrail events |
| Did a prompt or routing change improve the complete task? | Dataset and repeatable evaluation run | Per-case scores, failures, and comparison with a baseline |
| Can the agent complete a realistic conversation before launch? | Simulated multi-turn evaluation | Conversation transcript, state transitions, tool outcomes, and rubric scores |
| How is the deployed agent behaving with real users? | Evaluation of existing conversations or traces | Sampled interactions, safety results, task outcomes, and drift over time |
This separation prevents a common mistake: using an LLM judge to test code that could have been checked exactly, or assuming a green unit-test suite proves behavior owned by a remote model.
Layer 1: deterministic tests for application-owned behavior
Deterministic tests are the fastest feedback loop. They should exercise routing, tool wrappers, handoffs, guardrails, retries, streaming assembly, session behavior, and other orchestration that your SDK or application controls. Keep the input and expected event sequence fixed so a failure points to a code change rather than random model output.
OpenAI Agents SDK testing utilities
The OpenAI Agents SDK documents testing utilities that run in memory and make no model, sandbox-provider, or Realtime API requests. That makes them provider-neutral checks for SDK-owned interaction records. The documented areas include tool execution, handoffs, guardrails, retries, streaming, session behavior, Sandbox capabilities, Realtime events, and Voice pipeline composition.
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A typical test creates a controlled request, supplies fake tool results or a stubbed model decision where appropriate, runs the agent, and asserts on the resulting events. Useful assertions include:
- The expected tool was selected and an alternative tool was not called.
- Arguments were validated and transformed into the schema your backend expects.
- A handoff occurred only when its condition was met.
- A guardrail stopped the run and returned the intended safe response.
- A transient failure triggered the configured retry behavior without duplicating a non-idempotent action.
- Streaming events and session state were assembled in the correct order.
These utilities do not test the behavior of the external model, sandbox provider, or Realtime service itself. For that boundary, use a real provider adapter or an integration environment. A local test that replaces the model with a perfect stub can verify your handling of a tool call; it cannot establish that the production model will make that call.
Microsoft Agent Framework local checks
Microsoft Agent Framework documents a LocalEvaluator for checks that run without API calls. It is intended for inner-loop development, CI smoke checks, and fast iteration. The documented checks include keyword assertions and tool-called assertions. These are useful when a team needs a quick pass/fail gate, such as requiring a refusal phrase for a blocked request or verifying that a named tool was invoked.
Local checks are deliberately narrow. A keyword match can confirm that a required term appears, but it does not establish that the answer is correct or well grounded. A tool-called check can confirm invocation, but not whether the arguments were semantically appropriate. Add deeper evaluators only for criteria that the local assertion cannot observe.
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- Freeze the scenario. Use a short request and explicit fixtures for tool responses, session state, and expected errors.
- Record the event contract. Define the permitted sequence of model-independent events: route, handoff, guardrail, tool call, retry, and finalization.
- Assert side effects separately. Check the outbound payload and the resulting state, not just the final natural-language message.
- Include negative cases. Test missing arguments, unauthorized tools, malformed tool output, timeouts, and duplicate requests.
- Run the suite on every change. Keep these checks fast enough for pull requests, then reserve provider-backed evaluations for scheduled or explicitly requested runs.
Layer 2: traces explain what happened
A final answer hides the path an agent took. A trace preserves that path so an engineer can distinguish a bad instruction from a bad tool result or an incorrect handoff. OpenAI describes traces as end-to-end records of model generations, tool calls, guardrails, handoffs, and custom events. Tracing is enabled by default in the Agents SDK documentation, which also describes ways to disable it when required.
Inspect traces when a run is surprising, slow, unsafe, or expensive. Look for the first divergence from the intended workflow:
- Did the model receive the instruction and state you expected?
- Was the correct tool available at the decision point?
- Were arguments changed between generation and execution?
- Did a guardrail run before the side effect, or only after it?
- Did a retry hide an underlying timeout or duplicate a write?
- Did a handoff lose context or produce a loop?
Tracing is diagnostic evidence, not automatically a quality score. A trace viewer can show that a tool was called; it does not by itself tell you whether that was the best tool for the user’s goal. For that, apply a grader with an explicit criterion.
Layer 3: graders score workflow behavior
Trace grading turns a run into a structured judgment. OpenAI’s evaluation guidance describes grading traces for questions such as whether the agent selected the right tool, made an appropriate handoff, followed instructions or safety policies, and changed end-to-end behavior after a prompt or routing modification.
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Design a grader around an observable failure criterion rather than a vague request to “rate the agent.” A useful grader specifies the evidence it may inspect, the allowed labels, and examples at each boundary. For example:
- Tool choice: pass only when the selected tool can satisfy the request and no safer, required tool was skipped.
- Argument quality: pass when all required fields are present, correctly typed, and supported by the conversation.
- Handoff: pass when the destination agent owns the task and receives the context needed to continue.
- Policy adherence: fail when a prohibited action is attempted, even if the final prose looks harmless.
- Task outcome: pass when the requested state change or answer is achieved, not merely when the response sounds fluent.
Use an LLM judge where semantic interpretation is necessary, but retain exact checks for exact facts. A judge should be reviewed against labeled examples; its score is evidence, not ground truth. Microsoft recommends reviewing generated rubrics instead of accepting an aggregate judge score without inspection.
Datasets and repeatable evaluation runs
Selected traces are excellent for debugging a single incident. They become a regression system only after you assemble a representative dataset and rerun it consistently. OpenAI’s platform guidance describes moving from inspecting and grading traces to datasets and evaluation runs for benchmarking prompt changes, comparing routing strategies, and measuring behavior over time.
Build the dataset from real failure modes
- Include successful, borderline, and failed interactions; a dataset containing only incidents exaggerates failure rates.
- Record the user goal, relevant context, expected tool or outcome, safety classification, and any acceptable alternative.
- Keep sensitive data minimized and access controlled. Redact secrets before a trace becomes a reusable test case.
- Version the dataset with the agent instructions and tool schemas that produced it.
- Tag cases by capability, risk, language, and dependency so a score can be sliced rather than averaged into one opaque number.
Run a comparison that can support a decision
- Choose a baseline prompt, model configuration, or routing policy and run the complete dataset.
- Store per-case outputs, trace-derived evidence, grader labels, and errors; do not retain only the mean score.
- Run the candidate change against the same cases and evaluator version.
- Review regressions individually, especially safety failures and incorrect side effects.
- Promote the candidate only when it improves the target metric without violating a non-negotiable guardrail.
Repeatability matters more than a single impressive score. A changing dataset, evaluator rubric, or model version can make two runs incomparable, so record those variables with each evaluation.
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Single-turn prompts miss the ways an agent loses context, asks an unnecessary question, repeats a tool call, or makes a premature side effect. Microsoft Foundry distinguishes full-conversation evaluation from individual-turn evaluation. Its documentation supports simulated data for controlled pre-deployment scenarios and existing conversations or traces for evaluation of previous interactions. Full-conversation evaluation is labeled preview in that guidance, so confirm its current availability and behavior before making it a release gate.
When to simulate
Use simulated conversations when you need controlled coverage before users are exposed to a workflow. Define the user goal, hidden state, permitted actions, expected milestones, and stopping conditions. Vary one factor at a time—such as an ambiguous date, a missing account field, or a tool timeout—so the resulting failure remains diagnosable.
When to use individual-turn evaluation
Turn-level evaluation is appropriate for a focused response or tool decision, especially when the surrounding conversation is already fixed. It is faster and easier to label, but it cannot measure whether the agent maintained the correct state across the whole exchange.
When to evaluate existing conversations
After deployment, sample real interactions or stored traces to find distribution shifts and new failure modes. Existing conversations reveal phrasing, interruptions, and tool conditions that a pre-launch simulator may not have anticipated. Apply privacy, retention, and access controls before using production data for evaluation.
Rubrics and evaluator portfolios
Microsoft’s agent-evaluation guidance recommends a rubric evaluator as the primary measure, then additional evaluators for the dimensions the rubric cannot cover. Depending on the task, those may include task completion, response quality, text similarity, safety, tool use, or a custom criterion.
Write the rubric before looking at the candidate scores. Define what a pass, partial result, and failure mean, and include counterexamples. If “grounded answer” matters, state which sources or tool outputs count as support. If “safe” matters, list the prohibited action and the acceptable refusal behavior. If “efficient” matters, define a maximum number of calls or turns rather than asking a judge to infer efficiency from prose.
Combine evaluators without hiding critical failures. A weighted average can allow a severe safety violation to disappear behind high quality scores. Use hard gates for safety and unauthorized side effects, and report task and quality metrics separately.
How the documented tool families differ
The following comparison uses capabilities described in the vendors’ documentation. The sources do not establish a comparable price, latency, accuracy benchmark, or feature-limit matrix, so those cells are intentionally marked as not stated.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →| Tool family | Deterministic/local checks | Trace visibility and grading | Conversation scope | Data and hosting emphasis |
|---|---|---|---|---|
| OpenAI Agents SDK testing utilities | In-memory, provider-neutral utilities for SDK-owned orchestration; no model, sandbox-provider, or Realtime API requests | Pairs with OpenAI tracing and trace grading for model calls, tools, handoffs, guardrails, and custom events | Event and session behavior locally; full workflow analysis through traces and eval runs | Tracing is enabled by default in the documented SDK flow; unavailable for organizations using OpenAI APIs under a Zero Data Retention policy |
| OpenAI Platform evaluations | Not the primary focus of the documented progression | Trace inspection and grading, followed by datasets and repeatable evaluation runs | Selected traces first, then larger repeatable datasets | Check current data-retention and regional requirements for the organization |
| Microsoft Agent Framework | LocalEvaluator runs without API calls; documented keyword and tool-called checks |
Cloud LLM-as-judge options through Foundry-backed evaluators, including behavior, tool use, quality, and safety categories | Local checks are scenario-specific; broader scope depends on the connected Foundry evaluation path | Verify the service, region, retention, and data-handling settings selected for the deployment |
| Microsoft Foundry evaluation | Not stated as the main purpose of the portal guide | Rubric, task, quality, text-similarity, safety, and custom evaluators | Individual turns and full conversations; simulated data, datasets, existing conversations, and existing traces are documented options; full-conversation evaluation is preview | Use simulated data for controlled pre-deployment checks and existing interactions for post-deployment evaluation |
| Arize Phoenix | Supports deterministic code-based evaluators | Supports LLM-as-judge evaluators, OpenTelemetry-traced evaluator runs, and a trace viewer for evaluator decisions | Supports tool-calling-agent evaluation; exact conversation modes depend on the implementation | Documentation points to Arize AX for continuous production monitoring with alerting and threshold triggers |
Because these families emphasize different layers, a team may use more than one: local checks in CI, a trace system for diagnosis, and a dataset runner for release comparisons. That combination is often more practical than forcing every test into one interface.
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A practical buying and implementation workflow
1. Define the release risks
List the actions that must never occur, the outcomes that must occur, and the quality issues users will notice. Separate safety and authorization requirements from softer measures such as tone. This list becomes the evaluator portfolio and prevents a tool choice driven by feature names.
2. Establish the local gate
Implement deterministic checks for routing, tool schemas, handoffs, retries, guardrails, session transitions, and side effects. Run them in CI with no provider calls where the framework supports that mode. Fail fast on unauthorized tools, malformed arguments, and duplicate writes.
3. Capture representative traces
Enable tracing in a development or evaluation environment, then inspect successful and failed runs. Confirm that model generations, tool calls, handoffs, guardrails, and custom events contain enough context to diagnose a defect. If your organization’s retention policy disallows the tracing service, select a compliant alternative or disable tracing and rely on approved telemetry.
4. Add targeted graders
Start with a small number of criteria tied to the risks you listed. Use exact assertions for names, counts, schemas, and authorization. Use rubric-based or LLM-judge evaluation for semantic correctness, groundedness, and conversational quality, and review the judge’s examples.
5. Create simulated conversations
Before deployment, simulate normal, ambiguous, adversarial, and dependency-failure paths. Include enough turns to test state retention and handoffs. Keep the scenario definitions versioned so a change in the simulator does not masquerade as an agent improvement.
6. Promote failures into a regression dataset
Every confirmed defect should become a labeled case with the expected outcome and evaluator rationale. Run the dataset against a baseline and candidate before changing production prompts, tools, or routing.
7. Monitor real interactions
After launch, evaluate sampled conversations or traces for drift. Track task completion, tool-choice accuracy, response quality or groundedness, and safety separately. Feed novel failures back into the dataset and add a deterministic check when the failure is application-owned.
Hosting, retention, and regional constraints
Data handling can eliminate an otherwise attractive tool. OpenAI states that Agents SDK tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy. Treat that as a deployment constraint, not a minor configuration detail. For every candidate, confirm where traces and prompts are stored, how long they are retained, which processors can access them, whether regional processing is available, and how deletion works.
Best Value
Also classify the data in each evaluation path. Simulated cases can be designed without personal information. Existing conversations may contain account details, secrets, or regulated content and should be minimized, redacted, or excluded. A local evaluator that makes no API call may be the only permissible option for a sensitive inner-loop test, while a cloud judge may require an approved sanitized dataset.
Common mistakes and how to correct them
Using only final-answer snapshots
Symptom: a response looks acceptable, but the agent used an unauthorized tool or made an unsafe intermediate call.
Fix: grade the trace and assert the event sequence, not just the final text.
Using an LLM judge for exact contracts
Symptom: scores vary for a check such as “was tool X called once?”
Fix: use a deterministic tool-called, schema, or call-count assertion; reserve semantic judging for meaning.
Testing only one turn
Symptom: isolated prompts pass while a real conversation loses context or loops.
Fix: add simulated full-conversation scenarios and evaluate state transitions and handoffs.
Treating a judge score as ground truth
Symptom: a higher aggregate score is accepted despite obvious regressions in a critical case.
Fix: review the rubric, examples, per-case evidence, and hard safety gates before comparing averages.
Building a dataset from only failures
Symptom: the regression score falls even when users report improvement.
Fix: include representative successes, borderline cases, and failures, with tags that allow separate slices.
Ignoring provider boundaries
Symptom: local tests pass, but production behavior changes after a model or provider update.
Fix: run integration evaluations with the real adapter and maintain a dataset for provider-backed behavior.
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Symptom: a team chooses a tool based on an unverified price or accuracy claim.
Fix: compare the documented testing layer, evaluator data, integration, and compliance fit; measure your own workloads.
Decision checklist
- Do we need no-network, deterministic checks for application-owned orchestration?
- Can the system capture model calls, tool calls, guardrails, handoffs, and custom events?
- Can graders inspect the trace evidence relevant to our failure criteria?
- Do we need individual-turn checks, full multi-turn conversations, or both?
- Will our data come from curated cases, simulated scenarios, existing conversations, or traces?
- Can we define a rubric and review judge examples rather than trusting one score?
- Which framework and model providers must the tool integrate with?
- Are retention, regional processing, Zero Data Retention, and deletion requirements satisfied?
- Can every release run the same dataset with a recorded baseline and evaluator version?
Bottom line: buy for the workflow you need
Choose deterministic local testing when the risk is in your routing, tools, handoffs, guardrails, retries, or state code. Choose tracing and trace grading when you need to understand and score a real agent run. Choose datasets and repeatable eval runs when you need defensible regression comparisons. Choose simulation for controlled, multi-turn pre-deployment coverage, then evaluate existing conversations after launch. The strongest setup combines those layers and selects a platform only after its evaluator data, integrations, and retention model fit the agent you actually operate.
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