How to Turn Production Sessions into Regression Tests for Your AI Agent

Description and use

Leverage production logs in ZizkaDB to build regression tests that keep fixed incidents from reappearing and maintain continuous agent quality.

Problem

Production failures are often discovered only after release, because test suites are written manually and miss real user interactions.

Outcome

By converting production sessions into structured regression tests, teams can detect regressions that synthetic tests miss, maintain a living test suite that evolves with the agent, and ensure that fixes remain effective.

Workflow

• Define regression criteria (outcome, path, tools, cost, safety).
• Verify session completeness (linked events, named tools, prompt version, token usage, context).
• Select valuable sessions (failures, escalations, expensive, drift, patterns, normal).
• Review each session to identify first divergence and its impact using ZizkaDB why/impact APIs.
• Verify chain integrity with db.verify_chain and export relevant events with db.export_events.
• Redact personal data and replace with synthetic placeholders.
• Freeze case by capturing inputs, context, tool stubs, expected results, and metadata.
• Run the test suite on a disposable instance, importing events, stubbing tools, logging with idempotency, and asserting outcomes.
• Handle non‑determinism by running cases multiple times, tracking pass rates, and setting thresholds.
• Continuously grow, prune, and review the suite as new incidents occur.

Evidence

Your best source of test cases is already sitting in your logs. Every real failure, escalation, and dispute is a case your agent has actually met.

Source

ZizkaDB links every event into a per-agent hash chain, and a verify call checks the whole chain and every checksum.

Source

ZizkaDB replaces values under secret-named keys, such as password and api_key, with [REDACTED] before storage.

Source