What is agent testing?
Agent testing is the practice of checking that an AI agent reasons, acts, and completes tasks correctly before it runs against real systems and users.
Read → FundamentalsWhy is agent testing important?
Agent testing is important because agents take real actions with real consequences, and an untested agent fails in ways that cost money, corrupt data, or break trust.
Read → FundamentalsWhat is an API sandbox?
An API sandbox is an isolated copy of a real service that behaves like production so you can test against it safely.
Read → FundamentalsWhat is the difference between mocking an API and using a real sandbox?
A mock returns whatever you told it to return. A real sandbox holds state, enforces rules, and fails like production does.
Read → FundamentalsWhat are the four layers of agent testing?
The four layers are model evaluation, tool-call evaluation, workflow evaluation, and environment simulation. Most teams stop at layer two.
Read → FundamentalsWhy does agent testing need a real-world sandbox?
Because the risky part of an agent lives between systems, and you cannot see those failures unless you recreate the environment it runs in.
Read → Evaluation & metricsHow do you evaluate an LLM agent?
Evaluate an LLM agent by scoring its reasoning, its tool calls, and its end-to-end task success against a fixed set of representative scenarios, run many times.
Read → Evaluation & metricsWhat metrics matter for agent testing?
The metrics that matter most are task success rate, tool-call accuracy, workflow completion, failure recovery, and cost and latency, tracked across repeated runs.
Read → Evaluation & metricsWhat is LLM-as-a-judge and when should you use it?
LLM-as-a-judge uses a language model to grade an agent's output against a rubric, which scales evaluation for open-ended tasks that have no single correct answer.
Read → Evaluation & metricsWhat is a golden dataset and how do you build one for agents?
A golden dataset is a curated set of representative tasks with known-good outcomes that you use as a stable benchmark for evaluating and regression-testing an agent.
Read → Evaluation & metricsWhat benchmarks exist for evaluating AI agents?
Public agent benchmarks include SWE-bench, WebArena, tau-bench, GAIA, AgentBench, and the Berkeley Function-Calling Leaderboard, each targeting a different agent skill.
Read → Evaluation & metricsHow do you test an AI agent for hallucinations?
Test for hallucinations by checking the agent's claims and actions against ground truth, and by giving it tasks where the correct answer is to admit it does not know.
Read → Evaluation & metricsHow do you handle non-determinism in agent tests?
Handle non-determinism by running each scenario many times and asserting on success rates and invariants rather than on an exact output.
Read → Testing methodsWhat does the testing pyramid look like for AI agents?
For agents the pyramid is unit tests of tools at the base, integration tests against sandboxes in the middle, and full end-to-end scenario evals at the top.
Read → Testing methodsHow do you test an agent's tool calls?
Test tool calls by checking that the agent selects the right tool, passes valid and correct arguments, and handles the tool's real responses and errors.
Read → Testing methodsHow do you test multi-step agent workflows?
Test multi-step workflows by running the whole sequence against a stateful environment and asserting on the final state, including partial-failure cases.
Read → Testing methodsWhat is simulation testing for AI agents?
Simulation testing runs an agent inside a realistic, controllable model of its environment so you can observe how it behaves across many situations before production.
Read → Testing methodsWhat is regression testing for AI agents?
Regression testing for agents means re-running a fixed scenario set after any change to catch drops in success rate before they reach users.
Read → Testing methodsHow do you add agent testing to a CI/CD pipeline?
Run the agent against a fresh sandbox on every commit so integration failures appear in the pull request instead of in production.
Read → Safety & red-teamingHow do you red-team an AI agent?
Red-team an agent by actively trying to make it misbehave: adversarial inputs, prompt injection, permission escalation, and unsafe actions, then verify it refuses.
Read → Safety & red-teamingHow do you test an agent for prompt injection?
Test for prompt injection by planting hostile instructions in the content the agent reads and confirming it follows its real instructions rather than the injected ones.
Read → Safety & red-teamingWhat are guardrails and how do you test them?
Guardrails are checks that constrain what an agent can input or output, and you test them by trying to get prohibited behavior through and confirming they block it.
Read → Safety & red-teamingWhat are the most common AI agent failure modes?
The most common failures are wrong tool arguments, false success reports, broken state across steps, ignored permissions, and poor handling of rate limits and errors.
Read → Integration testingHow do you test a Stripe agent before production?
Test a Stripe agent using test mode, then push on refunds, disputes, webhooks, and idempotency where agents usually break.
Read → Integration testingHow do you test a Slack agent before production?
Test a Slack agent against real channel, thread, permission, and rate-limit behavior that a mocked client will never show.
Read → Integration testingHow do you test a GitHub agent before production?
Test a GitHub agent against branch protection, merge conflicts, and review state, which is where automation agents quietly go wrong.
Read → Integration testingHow do you test a HubSpot agent before production?
Test a HubSpot agent against deduplication, associations, and property validation, which matter more than the happy-path create call.
Read → Integration testingHow do you test a Salesforce agent before production?
Test a Salesforce agent against governor limits, validation rules, and required fields that reject writes which passed every unit test.
Read → Integration testingHow do you test a Discord agent before production?
Test a Discord bot against gateway events, intents, and permission hierarchies that decide whether it works or silently does nothing.
Read → Integration testingHow do you test a Gmail agent before production?
Test a Gmail agent against threading, labels, quotas, and send limits, which are the parts that break once real volume shows up.
Read → Production & monitoringHow do you know an agent is ready for production?
Stop asking whether the agent passed your tests. Ask whether it survived the environment it will actually operate in.
Read → Production & monitoringHow do you monitor AI agents in production?
Monitor agents by logging every action and outcome, tracking success and error rates over time, and alerting when behavior drifts from the baseline.
Read → Production & monitoringHow do you set up observability and tracing for AI agents?
Set up agent observability by tracing every step of a run as structured spans, using the OpenTelemetry GenAI conventions so traces work across tools.
Read → Tools & resourcesWhat are the best tools for testing AI agents?
A map of the agent testing ecosystem: evaluation frameworks, observability, red-teaming and guardrails, benchmarks, and sandbox environments, with links to each.
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