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Catch changes in agent behavior.

A prompt change can alter which tool your agent calls. Compare traces from the same scenario to see what changed.

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Compare behavior before and after a change

A release can preserve the answer while changing a tool argument. Keep the request and tool state fixed so you can compare both versions against the same rule.

Compare runs of a known scenario

Run the same fixtures yourself against each version, then send their recorded executions over OTLP for evaluation.

Production trace evaluation

After an interaction, send its recorded execution over OTLP. Mibo evaluates applicable configured checks without calling the agent again.

A bounded signal

Interpret results through the checks you define, the instrumentation your trace includes, input classification, and the breadth of scenarios you exercise or observe.

Compare versions on controlled fixtures

Compare the same expectation before and after a change.

  1. Hold the fixture constant

    Run both agent versions in your own system with the same request and controlled tool state. Record the prompt, model, and suite versions.

  2. Evaluate the recorded evidence

    Send both executions as traces and inspect the same configured checks. Compare the tool arguments and response criteria, rather than only the final wording.

  3. Review the neighboring cases

    After a fix, rerun the failed case plus an authorized success and a tool-failure case. Your team owns execution and release decisions; Mibo evaluates received traces.

Compare the response and the tool call

Semantic checks

Apply the same written response criterion to both versions. Check whether a changed answer still explains the outcome accurately.

Procedural checks

Compare the recorded tool name and account argument against the same expected values. A changed argument can fail the check even when the answer stays the same.

Frequently asked questions

What makes AI agent regression testing different from a one-time test run?

Regression testing compares the same defined behavior over changes. Run comparable scenarios yourself and evaluate the recorded traces against configured checks.

Does Mibo test an agent again when it evaluates a production trace?

After an interaction, send its recorded execution over OTLP. Mibo evaluates applicable configured checks without calling the agent again.

How does Mibo decide which regression tests apply to a real interaction?

Mibo uses the input available in the trace to classify which scenario-conditional tests are relevant. Universal checks can apply to every trace, while a test may be unavailable when the trace has no usable match or lacks the required evidence.

Can AI agent regression testing guarantee complete coverage?

No. Results are bounded by the scenarios and checks you configure, the interactions that occur, the classification signal, and the instrumentation and fields you send. Mibo does not claim complete coverage, guaranteed quality, or automatic remediation.

What happens when a check needs data that the trace does not include?

The outcome depends on the check. Some checks report missing instrumentation; others evaluate the missing value against their configured condition. Inspect the required fields and the check’s behavior before interpreting the result.

How does Mibo handle trace privacy?

Trace payloads are encrypted at rest, and product access is scoped to the account that owns them. Send synthetic or appropriately governed data that fits your own privacy requirements.

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