The Tool Desk
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Keep your unit and integration tests. Add tests that check what the agent does along the way: which tools it calls, whether it follows product rules, how it handles unexpected results, and whether it actually completes the task. An agent can return a plausible answer after taking an unsafe or invalid step, so a final-answer check alone is not enough.
Agentic systems are still software, but their behavior can vary with model-generated choices, multi-step trajectories, tools, context, and changing policies. Testing them well means extending conventional testing—not replacing it—with application-specific scenarios, trace checks, and repeated evaluations.
Why do agent tests behave differently from ordinary software tests?
Conventional tests work best when a known input should produce a predictable output. They remain essential for stable application code, API schemas, permissions, and tool contracts. An agent adds another layer: its model may choose different actions or sequences, and those choices can depend on the context, tool responses, prompts, and policies in force during a run.
That variability makes a test fail for more than one reason. The model may make a different choice on a later run, a tool may return unexpected content, or a policy or prompt change may alter the path. A changing trajectory is not automatically a software defect, but it does mean that a single pass/fail result can hide what happened.
There is also a difference between a successful-looking answer and a valid run. An agent might make an inappropriate tool call, violate a boundary, or take a wrong turn and then recover before producing a reasonable final response. Microsoft’s AgentRx overview argues that task completion alone does not provide enough information to debug agent behavior.
What should you test: the answer, the actions, or both?
Test the outcome and the process. Outcome checks establish whether the user’s goal was met; process checks establish whether the agent got there in an acceptable way. Which process checks matter depends on the product’s own policies, tools, and risk boundaries.
| Test layer | What it checks | Useful evidence |
|---|---|---|
| Unit and integration tests | Deterministic application logic, API schemas, authorization, and tool behavior | Expected values, valid or rejected inputs, and permission results |
| Behavior scenarios | Whether the agent follows requirements across ambiguous requests, policy boundaries, tool errors, and recovery | Actions, tool arguments and results, required steps, and policy compliance |
| End-to-end task evaluations | Whether the complete workflow achieves a realistic user goal | Task outcome plus the trajectory that produced it |
For example, a workflow that can take a consequential action should be tested both for its final result and for whether it requests confirmation when required. A scenario should also specify what evidence the agent must have before making a claim or tool call. These are product-specific requirements, not universal rules for every agent.
Microsoft’s Agent-Pex project describes evaluating traces for properties such as argument validity, output compliance, and plan sufficiency. Its project page reports analysis of more than 5,000 Tau² traces across three domains, comparing four models. Those results illustrate trace-level evaluation; they do not establish how another workload will perform.
How do you turn requirements into agent tests?
Start with observable behavior rather than a broad goal such as “be helpful.” Write down what the agent must do, what it may do, and what it must not do. Microsoft’s ASSERT approach treats requirements as inputs to scenarios, datasets, metrics, and scorecards. Its authors caution that generic signals such as helpfulness or groundedness may not directly test the boundaries that matter in a particular product. See Microsoft’s ASSERT overview.
- Specify the expected behavior. Include both positive requirements and forbidden actions. State when a workflow must ask for confirmation, what counts as sufficient evidence, and which tool actions are allowed.
- Make requirements checkable. Define what an evaluator should observe in the response or trace. “Use the approved source before making a claim” is more testable when the requirement identifies the relevant source and the evidence to verify.
- Build scenarios around real conditions. Include ordinary requests as well as ambiguous instructions, policy boundaries, malformed or misleading tool responses, and opportunities to recover from failure.
- Keep the specification current. Version the policies, prompts, tools, and retrieval context that shape the expected behavior. An evaluation can become stale when its product context changes.
ASSERT describes generating stratified test cases from a behavior taxonomy. The practical value of that idea is coverage: a set of scenarios should represent the distinct behaviors and boundaries in the specification, rather than repeatedly testing only the easiest path.
What should a useful agent scorecard measure?
Pair task success with checks on the actions that matter for the workload. A single completion score may not reveal whether the agent used valid arguments, followed required steps, respected access or approval constraints, or relied on tool results appropriately.
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- Tool use: Were the selected tools and arguments valid for the task?
- Required steps: Did the agent complete any mandatory checks or approvals?
- Boundaries: Did it avoid disallowed actions and respect access constraints?
- Evidence: Were decisions and claims supported by the relevant tool results?
- Recovery: When a tool response was unexpected or an action failed, did the agent handle it appropriately?
These checks need not collapse into one number. A scorecard can keep outcome and process results separate so a high task-success rate does not conceal a serious boundary failure. AgentRx describes synthesizing guarded constraints from tool schemas and domain policies, checking applicable constraints step by step, and logging evidence for violations.
In its March 12, 2026 article, Microsoft reports evaluating AgentRx on 115 manually annotated failed trajectories from τ-bench, Flash, and Magentic-One. In those reported experiments, it improved failure-localization accuracy by 23.6% absolute and root-cause attribution by 22.9% compared with prompting baselines. These are results for that evaluation setup, not a general guarantee for other agents or workloads.
How can you make variable agent runs reproducible?
Separate two goals: reproducing a result and broadening scenario coverage. Fixed scenarios and stable configurations help teams compare changes; varied scenarios can expose behaviors that a fixed set misses. For stochastic agents, report enough information to interpret a result rather than treating one run as definitive.
- Preserve the evaluation conditions. Record the task set, model and agent versions, prompts, tool configuration, environment, and scoring method.
- Repeat scenarios when variability matters. Report the number of runs alongside the result. A single run does not describe how often an outcome occurs.
- Use fixed inputs for controlled comparisons. Keep scenarios and configuration stable when the goal is to assess a model, prompt, or release change.
- Vary cases for broader coverage. Use additional or generated scenarios to probe different situations, and retain their configuration so the test can be understood later.
Anthropic’s Bloom evaluation tool varies generated scenarios across runs while supporting reproducibility through a configuration seed; its authors advise citing the seed with reported metrics. A seed helps identify the setup, but it does not make a stochastic agent’s behavior deterministic. Bloom also describes a human-rating validation exercise on 40 transcripts: the reported Spearman correlations were 0.86 for Claude Opus 4.1 and 0.75 for Claude Sonnet 4.5. Those figures apply to that validation context, not to every judge, rubric, or agent task. Details are in Anthropic’s Bloom article.
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How do you tell why an agent failed?
Keep an auditable trace of the run, not just the final response. The useful record is the sequence of relevant decisions and events: agent actions, tool inputs and outputs, constraints that applied, and the task outcome. That gives a reviewer a way to locate where the run first became invalid or unrecoverable.
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When investigating a failure, inspect the trajectory in order:
- Find the first action that violated a requirement or departed from the expected plan.
- Check the context available to the agent at that point, including the preceding tool response.
- Determine whether the cause was an invalid tool call, a missed constraint, misleading output, or an inability to recover.
- Turn the finding into a scenario that can be rerun, and update the relevant requirement or evaluator if needed.
AgentRx’s guarded constraints and evidence-backed violation logs are designed to support this kind of diagnosis. A useful trace lets teams distinguish a bad final answer from an earlier action that made the run unsafe, even if the agent later recovered.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you interpret benchmark scores?
A benchmark score describes performance on the benchmark and conditions that were tested. It does not, by itself, establish performance on every similar task or in a different production environment. The distinction matters because a fixed collection of test items is only a sample of the work an agent may encounter.
The National Institute of Standards and Technology (NIST) formally distinguishes benchmark accuracy—performance conditioned on a fixed benchmark—from generalized accuracy across potential test items similar to those in that benchmark. Its February 17, 2026 study examines statistical models using 22 API-access frontier language models across three benchmarks, and discusses modeling uncertainty and item difficulty. Read NIST’s overview of statistical models for AI evaluation.
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Keep benchmark scope, conditions, and uncertainty visible when comparing releases. A small score difference should not automatically be treated as meaningful without considering the task sample and run variability. For example, OpenAI’s ChatGPT Agent System Card reports results on a fixed subset of 477 SWE-bench Verified tasks validated on internal infrastructure; for specified model settings, it describes averaging four tries per instance. That is evidence about the stated tasks and procedure, not a blanket estimate for all software-engineering work or agent deployments.
What counts as enough testing before release?
There is no generally valid pass threshold, minimum number of stochastic trials, or universal test pyramid for agents established by these sources. Set release criteria according to the risk and purpose of your own workload, then state the assumptions behind the evaluation.
- Require the ordinary deterministic checks for code, schemas, permissions, and tools that can be tested conventionally.
- Cover application-specific behaviors and forbidden actions with scenarios tied to versioned requirements.
- Review traces for process failures, not only failed outcomes.
- Repeat variable cases and record the conditions needed to interpret the results.
- If an automated language-model judge is part of the evaluation, compare it with human ratings on representative examples, review disagreements, and retain the rubric and judge version.
- Report what the benchmark or scenario set actually covers, along with uncertainty where relevant; do not present a bounded evaluation as proof of production readiness.
Agent tests are most useful when they make the system’s expected behavior explicit and leave enough evidence to explain what happened. Conventional tests continue to protect the stable parts of the software; trajectory and behavior evaluations address the decisions that ordinary input-output checks cannot fully capture.
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