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Test safety-related agent behavior before release and inspect evidence after real interactions.

Mibo helps teams turn safety expectations into active checks before release, then evaluate applicable passive traces after real customer interactions. The evidence is bounded by your configured checks, instrumentation, classification, and observed scenarios.

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Safety risk is hard to verify when a response looks fine on the surface.

Teams responsible for customer-facing agents need to check what the agent says and what it does in safety-sensitive situations. Mibo keeps those checks tied to defined scenarios instead of turning a few results into a general safety claim.

Customer-facing scenarios

Define the safety-related situation, input, and behavior you expect so a test case reflects a concrete release concern rather than generic safety advice.

Semantic behavior

Write criteria for a response to refuse, clarify, disclose uncertainty, or stay within an approved scope. The evaluator scores the response and context available to it.

Procedural guardrails

Inspect supported facts such as required or forbidden tool calls, arguments, response fields, status, timing, or token usage when the active result or trace contains the needed fields.

From a safety expectation to bounded evidence

Make a focused safety case, combine the checks that express its behavior, and review what the available evidence can support. Active runs and passive traces answer different questions.

01

Define the case

Describe the customer situation and input, then state what the agent should do or avoid. Include the edge condition that matters for release.

02

Combine checks

Use semantic criteria alongside procedural checks such as a required or forbidden tool call, an argument, a status, or a response field. Configure only the evidence your system can expose.

03

Test and inspect

Run a selected case against a connected Agent before release. When eligible active cases match canonical traces from real interactions, Mibo evaluates them asynchronously without calling the Agent again.

Use active and passive testing to inspect the same defined behavior

Semantic safety checks

Describe what a safe, appropriate, honest, or in-scope response should mean. The score is evidence for review and depends on the response and context available to the evaluator.

Procedural safety checks

Verify deterministic facts such as a required or forbidden tool call, an argument, a response field, an HTTP status, a schema, a time limit, or token usage. Missing trace fields surface as missing instrumentation.

Frequently asked questions

What is AI agent safety testing?

It is the deliberate testing of safety-related behavior in a defined AI agent scenario. Mibo lets teams combine semantic criteria about the response with procedural checks on supported execution evidence.

How does Mibo support safety testing before release?

Active testing sends a selected scenario input to a connected Agent and evaluates the response and available execution evidence. The result is evidence for that configured case, not proof about unobserved situations.

Can Mibo inspect safety behavior after real interactions?

Yes, when an active test is eligible for passive evaluation and your system sends an applicable canonical trace. Mibo evaluates it asynchronously without calling the Agent again.

What safety-related checks can teams configure?

Teams can describe response expectations with semantic assertions and check supported facts such as required or forbidden tool calls, arguments, response fields, HTTP status, schema, timing, or token usage. The available evidence depends on the active result or trace.

What if a safety check needs data that the trace does not contain?

Mibo reports missing instrumentation for supported checks that cannot find the required trace field instead of silently treating the check as passed or failed. Add the relevant fields if that evidence matters to your workflow.

Does AI agent safety testing guarantee that an agent is safe?

No. Configured checks provide bounded evidence, not a safety guarantee or automatic remediation. The signal is limited by your scenarios, checks, inputs and traces, classification, instrumentation, and the breadth of interactions you exercise or observe.

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