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Test a tool-using agent as a running system, not as a text generator. Give it a defined task and starting state, let it operate through the real multi-turn tool loop in an isolated environment, preserve the complete trace, verify the resulting state independently, and grade both the path and the outcome. Repeat representative cases across trials and after every model, prompt, tool, or routing change.
Why a final answer is not enough
An agent can reply “Your order has been cancelled” without cancelling anything. It can call a plausible tool with the wrong identifier, skip a required approval, expose data it should have protected, or reach the right result by an unsafe route. A single response cannot show which tools were selected, what arguments were sent, whether calls happened in the required order, or what changed in the environment.
Anthropic defines an evaluation as giving an AI an input and applying grading logic to its output to measure success. For an agent, “output” includes the trajectory: model turns, tool calls, tool results, guardrail decisions, handoffs, and state changes. The practical unit of testing is therefore an end-to-end trial with an observable success condition.
This distinction matters for any workflow that reads or changes external state: booking, order support, account administration, file operations, data entry, code execution, or a handoff to another agent. Grade the user-visible response, but also check what actually happened.
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The evaluation loop
A reliable test cycle has six stages:
- Specify the case: define the user goal, initial state, available tools, constraints, and independently observable expected result.
- Run the real loop: execute the same prompts, routing, tool schemas, and turn handling used by the application, inside a controlled environment.
- Capture the trace: store model inputs and outputs, tool names and arguments, results, guardrails, handoffs, and relevant state transitions.
- Grade trajectory and outcome: assert important intermediate decisions and verify the final environment state separately from the final message.
- Inspect failures: classify the first wrong decision, the system condition that enabled it, and whether recovery worked.
- Repeat and compare: run multiple trials for variable behavior and maintain a regression dataset for every change.
Do not collapse these stages into a single pass rate. A useful report can show, for example, that 92% of cases reached the desired state, 97% selected an allowed tool, 89% supplied exact arguments, and 84% followed the required sequence. Those numbers answer different engineering questions.
1. Define tasks and observable success
Write cases from real user goals
Start with tasks your product must perform, not a generic benchmark. Include common requests, high-risk actions, ambiguous instructions, and known edge cases. A case should state:
- the user message or conversation history;
- the initial environment state, such as database rows, files, account permissions, or mocked API responses;
- the tools exposed to the agent, including schemas and authorization limits;
- system and business constraints, such as approval requirements or data-handling rules;
- the expected observable result and any acceptable alternatives;
- the checks that determine pass, partial pass, or failure.
Google Cloud describes a case as a task specification that can include conversation steps and context. OpenAI recommends defining the evaluation objective and collecting data appropriate to it. Together, these ideas prevent a vague instruction such as “handle the customer” from becoming an unscorable test.
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Cover every decision about calling a tool
Include cases in which the correct behavior is to act, ask a clarifying question, refuse, or finish without a tool call. A status request may require an order lookup; a message with no order number may require clarification; a request to expose another person’s private data may require refusal; a casual question may need no tool at all.
For each case, record whether invocation is required, optional, or prohibited. Then test both the decision and the details. OpenAI’s order-status example evaluates whether the lookup tool is invoked and whether the correct order ID is extracted. A tool call with an invented or mistyped ID is a failure even if the final prose sounds helpful.
Define success as state, not intention
Use an independent oracle whenever possible: a database record, file contents, API resource, completed workflow, or validated artifact. For a booking task, the assertion is that a reservation exists with the correct date and customer, not that the agent claimed to book it. For a file-editing task, compare the resulting file or a structured checksum. For a support handoff, verify that the receiving queue contains the case and that required context was transferred.
Allow more than one valid path when the user outcome is what matters. Anthropic notes that an agent may find a better solution than a static evaluation anticipated. Use strict path assertions only for genuine requirements, such as a mandatory approval, a safety check, or an API call that must precede a destructive operation.
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Use the production loop where practical
Run the same agent harness used by the application: identical system instructions, model configuration, tool definitions, authentication boundaries, routing, memory behavior, and turn limits. A unit test that calls a function directly does not reveal whether the agent chose that function, extracted arguments correctly, or recovered after a failed call.
For actions that could affect customers or production data, use mocks, isolated test accounts, snapshots, or a simulator. The test environment should expose realistic schemas and permissions while making reset and inspection easy. Every run needs a known starting state so that one trial cannot contaminate the next.
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Simulate failures deliberately
Ordinary success paths are necessary but insufficient. Inject conditions that a robust agent must handle:
- malformed, empty, or incomplete tool results;
- transient HTTP 503 responses and retryable timeouts;
- latency spikes and out-of-order conversational turns;
- permission errors or expired credentials;
- ambiguous or contradictory user instructions;
- changes of intent in a later turn;
- partial completion followed by a failed downstream call.
Google Cloud documents environment simulation that can inject mocked data, errors such as HTTP 503, and latency spikes without touching production backends. Match injected faults to your own risk profile; a generic failure list is not a substitute for understanding how your tools fail.
Make resets and isolation explicit
Before each trial, seed the database, files, queues, and conversation state. Give each run a unique test identifier and record the environment version. Afterward, snapshot the final state or export the relevant records so graders can inspect them without querying a moving target. Ensure test credentials cannot reach production and that destructive tools are either mocked or constrained to disposable resources.
3. Capture a complete trace
OpenAI’s agent-evaluation documentation defines a trace as the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run. Anthropic uses “transcript” or “trace” for the complete record of a trial, including intermediate results. Google Cloud likewise describes traces as records of inputs, responses, and tool calls. Your logger should make the same evidence available in one replayable record.
Minimum trace fields
- case ID, run ID, timestamp, model and configuration version;
- system, developer, and user messages supplied at each turn;
- model responses, including tool-call decisions and final text;
- tool name, exact serialized arguments, authorization context, and call order;
- raw or normalized tool result, error code, latency, and retry count;
- guardrail evaluations, policy blocks, confirmation prompts, and overrides;
- handoff source, destination, transferred context, and receiving result;
- relevant state before and after each mutating operation;
- token or inference counts and total duration when available.
Redact secrets and personal data while preserving the fields needed to diagnose behavior. Keep a schema version so old traces remain interpretable after instrumentation changes. Store enough context to replay a failure, but do not log credentials, payment data, or unrestricted production records.
Why intermediate evidence matters
Suppose the final state is wrong. The trace distinguishes a bad plan from a bad tool, an argument-extraction error from a stale result, and a policy block from a timeout. Without that distinction, teams often change prompts when the real defect is an incorrect schema or an unreliable backend.
4. Grade the trajectory
Tool selection and arguments
Assert whether the selected tool was appropriate for the user intent. Then validate arguments against the case’s expected values and schema: identifiers, dates, quantities, filters, recipients, and authorization scope. A correct tool with an incorrect argument is not a near-perfect result when it could modify the wrong record.
Order, repetition, and recovery
Some workflows impose a partial order. Authentication may precede account changes; a read may precede a destructive write; confirmation may be mandatory before sending. Check required calls in order, while allowing harmless extra reads if your policy permits them. Also check retry behavior: did the agent retry a transient failure, avoid retrying a non-idempotent mutation, and stop after the configured limit?
Instruction following and safety
Evaluate system instructions, data boundaries, and refusal behavior explicitly. Test prompt-injection content in tool results, requests for unauthorized data, attempts to bypass confirmation, and instructions that conflict with policy. A safe refusal should not call a prohibited tool or leak the protected value while explaining the boundary clearly.
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Handoffs and multi-agent boundaries
If the system routes work between agents or to a human, grade the handoff condition, destination, and transferred context. The receiving party should get the facts needed to continue without duplicated or contradictory actions. A handoff that occurs too early, too late, or to the wrong specialist is a trajectory failure even if the original agent’s final message is polished.
Use deterministic checks first
Use exact assertions for state changes, required arguments, schema validity, mandatory tool order, and policy blocks. These checks are reproducible and easy to debug. For qualities without a trusted reference—clarity, completeness, or helpfulness—use a rubric or model judge, then calibrate it against human ratings. OpenAI warns against relying on generic or uncalibrated metrics; Google ADK documents rubric-based quality and tool-use measures as separate dimensions.
5. Verify the environment independently
Grade the final response and the external result as separate dimensions. A truthful response can report failure correctly; that is different from successful task completion. Conversely, an agent can reach the correct state while falsely claiming a different action or omitting a required disclosure.
Examples of independent oracles
| Task type | State assertion | Response assertion |
|---|---|---|
| Booking | A reservation with the required customer, resource, and time exists exactly once. | The confirmation identifies the actual reservation and does not invent details. |
| Order support | The lookup used the requested order and returned the current status. | The answer matches the returned status and asks for clarification when the ID is missing. |
| File operation | The expected file exists with the required contents and permissions. | The agent accurately states what changed and what it could not change. |
| Handoff | The receiving queue contains the case and required context. | The user is told whether a handoff occurred and what to expect next. |
When an exact oracle does not exist, define a rubric before running the test. Specify required facts, unacceptable claims, and acceptable alternatives. Have humans review a sample of automated grades to detect systematic false positives or false negatives.
6. Repeat trials and report variation
Model behavior is not necessarily deterministic. Run multiple trials for nondeterministic tasks, especially those involving planning, ambiguous language, generated scenarios, or retries. Report the number of trials, pass-rate calculation, confidence or spread where useful, and representative failures—not only the best run.
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Use a fixed trial count for routine regression runs and a larger sample for release decisions or high-risk workflows. Keep failed traces, not just aggregate scores, so a rare but severe safety violation remains visible even when the overall success rate is high.
7. Turn traces into a maintained evaluation set
Start with exploratory trace review
During early development, inspect individual traces to learn what “good” behavior looks like and to discover missing cases. Label the first wrong decision, the violated requirement, the environmental cause, and the user impact. This is faster than designing a large benchmark before the workflow is understood.
Promote representative cases to a dataset
Once behavior is clear, freeze representative cases with their starting states, tool catalog, expected outcomes, and graders. Include successful examples, near misses, safety refusals, recovery paths, and every production failure that reveals a new pattern. OpenAI recommends moving from individual traces to datasets and evaluation runs for benchmarking changes and comparing prompts.
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Run regressions after every relevant change
Re-run the suite after changing a model, prompt, tool schema, backend, router, memory policy, guardrail, or retry logic. Compare not only task success but also safety, trajectory quality, tool correctness, latency, and efficiency. OpenAI’s best-practices guidance recommends continuous evaluation and growing the dataset as new nondeterministic cases appear.
Keep an evaluation history with configuration, trial count, grader versions, and score definitions. A score is not comparable if the case set or grading logic changed silently. Mark intentional behavior changes and review any regression in high-severity cases before shipping.
What to measure beyond pass rate
Google ADK lists multi-turn task success, trajectory quality, tool-use quality, safety, and efficiency as distinct criteria. Its efficiency measures include tool-call count, inference-call count, token usage, and invocation duration. Track these dimensions separately:
| Dimension | Useful measures | Typical question |
|---|---|---|
| Outcome | State-change pass rate, artifact validity, completion rate | Did the user goal actually occur? |
| Response quality | Factual accuracy, completeness, clarity, calibrated uncertainty | Did the message describe reality correctly? |
| Tool correctness | Allowed-tool rate, argument precision, schema errors | Did the agent choose and parameterize the right operation? |
| Trajectory | Required-order compliance, unnecessary calls, recovery success | Was the path acceptable and efficient? |
| Safety | Unauthorized-action rate, refusal precision, data-leak incidents | Did it respect policy and permissions? |
| Efficiency | Tool calls, inference calls, tokens, duration, retries | What did each successful task cost in time and compute? |
Do not combine incompatible dimensions into one headline number unless stakeholders understand the weighting. A workflow with a slightly lower completion rate but zero unauthorized writes may be preferable to one with a higher average score and a serious safety failure.
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Different approaches answer different questions. Compare them on execution location, trace visibility, outcome checks, scenario support, scoring, repeatability, and operational reporting.
| Approach | Best use | Strengths | Limitations to test |
|---|---|---|---|
| Trace-first debugging | Early development and failure diagnosis | Fast inspection of calls, results, guardrails, handoffs, and state | Can miss coverage and make score comparisons laborious |
| Code-first dataset harness | Regression testing and CI | Versioned cases, deterministic assertions, repeatable comparisons | Requires ongoing fixture and grader maintenance |
| Simulated environment | External actions and resilience tests | Safe resets, mocked data, injected errors and latency | Simulation may not reproduce every production dependency |
| Generated behavioral evaluation | Exploring broad or changing behavior | Varied scenarios and multi-turn interactions; seeds can support reproduction | Needs careful scenario validation and may produce noisy scores |
| Hosted evaluation surface | Team-wide trace review and comparison | Centralized runs, traces, graders, and reporting | Check deployment fit, data governance, exportability, and cost |
No documentation reviewed establishes a neutral benchmark proving one platform universally best. Run a small pilot with your own representative tasks, risk controls, trace requirements, and operational constraints before standardizing.
Published examples and how to interpret them
Anthropic’s 2025 Bloom release reported results for four alignment-relevant behaviors across 16 models. That is an example of a behavioral evaluation release, not a general benchmark of agent task success; it should not be used as a substitute for testing your tools and environment.
OpenAI’s evaluation guidance gives an illustrative summary-evaluation example with 1,000 reference transcript summaries, a ROUGE-L target of 0.40, and an 80% coherence target. Those figures explain how a particular rubric might be specified. They are not universal thresholds for tool-using agents and should not be presented as such.
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A compact implementation checklist
- Define the user goal, starting state, tools, constraints, and observable expected result.
- Include act, clarify, refuse, and no-tool cases, plus multi-turn intent changes.
- Run the production-like loop in isolated accounts, mocks, or a simulator.
- Inject realistic tool errors, latency, malformed results, and permission failures.
- Log model turns, tools, exact arguments, results, guardrails, handoffs, and state transitions.
- Assert tool choice, argument precision, order, retries, safety, handoffs, and final response accuracy.
- Verify database, file, API, queue, or artifact state independently of the response.
- Use deterministic checks where possible and calibrated rubrics for subjective quality.
- Repeat variable tasks, record trial count and configuration, and retain failure traces.
- Promote representative cases into a versioned dataset and run it after every change.
- Report outcome, trajectory, safety, latency, token use, and call counts separately.
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“The response looked right, so the test passed.”
Cause: grading only the final text. Fix: query the resulting state and compare it with the expected artifact or record.
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“The agent called the right tool but changed the wrong record.”
Cause: no argument-level assertion. Fix: validate every identifier, date, quantity, recipient, and authorization field against the case.
“The pass rate changed unpredictably.”
Cause: one trial, undocumented sampling, or changing scenarios. Fix: run multiple trials, record configuration and seeds, and report the aggregation method.
“A prompt fix broke recovery.”
Cause: success-only cases and no injected failures. Fix: add transient errors, malformed results, retries, and partial-completion cases to the regression set.
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Cause: an uncalibrated model judge or vague rubric. Fix: define observable criteria, compare automated grades with human labels, and revise the grader before trusting trend lines.
Conclusion
The dependable test for a tool-using agent is an instrumented, repeatable trial: start from a known state, let the agent make its real multi-step decisions, preserve the full trace, verify the environment, and score the path as well as the result. Build a dataset from those cases, exercise it after every system change, and keep safety, efficiency, and failure evidence visible alongside task success. That process tests the system users rely on—not merely the sentence it produces.
Frequently Asked Questions
How many trials should I run for a nondeterministic agent?
Run multiple trials and report the count and aggregation method; increase the sample for high-risk workflows or release decisions. Record model settings, scenario configuration, and an evaluation seed when supported so failures can be reproduced.
Should every tool call have a fixed expected sequence?
Only when order is a real requirement, such as approval before a destructive write. For outcome-driven tasks, allow valid alternative paths while asserting required safety checks, arguments, and final state.
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Can a language model grade the agent?
Use a model judge for qualities without a trusted reference, such as clarity, with a written rubric and human calibration. Use deterministic assertions for state, schema, arguments, and mandatory policy behavior.
What belongs in a trace?
Record model inputs and outputs, tool names and exact arguments, results and errors, retries, guardrails, handoffs, state transitions, configuration, and timing or usage data. Redact secrets and personal data.
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