Happy-path bias
Scripted flows miss live language, partial tools, and messy state.
AI agent testing
AI agent testing that starts from production failures.
Go beyond scripted happy paths. Test tool use, state, recovery, and escalation on real traces—then turn reviewed failures into approved examples you can re-measure.
Most agent testing stops at pass/fail.
Simulated suites catch some regressions. Without approved replacements and production-grounded scenarios, testing does not improve the next similar run.
Answer-only asserts
Checking the final string hides unsafe trajectories.
No correction artifact
A failed test without an approved rewrite cannot teach runtime behavior.
Disconnected QA
Findings stay in tickets instead of becoming eval-ready experiences.
Testing connected to an experience library
We combine scenario design, production-trace replay, human-approved corrections, and before/after measurement on one agent workflow.
Production-grounded cases
Promote recurring live failures into versioned test scenarios.
Trajectory assertions
Score tools, state, recovery, and escalation—not only final text.
Fix-forward loop
Capture approved replacements and re-test with retrieval where appropriate.
What serious agent testing includes
Buyers need a release gate and a correction path—not another dashboard of red scores.
Scenario versioning
Lock prompts, tools, and expected trajectories so releases compare fairly.
Mixed modes
Offline replay, controlled simulation, and targeted live review for high-risk clusters.
Failure-to-gold path
Define when a failed test becomes an approved experience for evaluation or retrieval.
Deepen trajectory scoring with AI agent evaluationConnect tests to LLM evaluation services and release gates
Agent testing package
Turn production failures into versioned tests and approved fixes.
Scenario inventory
Map current tests against recurring production failures.
Trajectory assertions
Tools, state, recovery, escalation, and safety checks.
Failed-to-gold workflow
When a failed test becomes an approved experience record.
Regression gate outline
Version prompts, tools, and expected trajectories for release comparison.
Scorecard template
Start from the downloadable agent evaluation rubric CSV.
Reusable resource
Download the AI agent evaluation rubric
Use the CSV as a testing scorecard for outcome, tools, state, recovery, escalation, safety, cost, and latency.
Illustrative template—not client data. Adapt it to your policies, privacy controls, and approval process.
Who this is for
- Teams shipping tool-using agents who need regression gates
- Support, voice, and ops agents with repeating multi-step failures
- QA leads tired of pass/fail suites that do not store the fix
- Leaders comparing prompt patches vs retrieving approved experiences
Agent testing sprint
A — Audit
Map current tests vs production failures and define trajectory assertions.
B — Dataset
Versioned scenarios + approved corrections for one workflow.
C — Proof
Baseline vs changed agent comparison and a release recommendation.
Testing that changes the agent
A failed test is incomplete until there is an approved replacement and a re-measurement plan.
Limitations
- Simulation alone under-covers live language and tool noise.
- Pass/fail without an approved rewrite does not improve the next run.
- Over-broad suites dilute signal; start with one expensive workflow.
- Retrieval experiments need held-out measurement before production expansion.
Methodology by LTTAI LTD
This page describes less than three’s production-trace review method: reconstruct failures, capture human-approved corrections, store them outside model weights, and measure whether scoped retrieval improves a defined workflow.
Primary references used for terminology and risk framing:
Common questions
What does ai agent testing include?
We combine scenario design, production-trace replay, human-approved corrections, and before/after measurement on one agent workflow.
Do you fine-tune our model?
No. Approved corrections stay outside model weights and can be retrieved at runtime. Fine-tuning can still complement the system for stable, common behavior.
What do we need to start?
A sample of production traces or conversation exports for one workflow, plus a working definition of success for that workflow.
How is success measured?
We compare a fixed baseline against a changed agent on the same workflow using task success, edits, escalations, latency, and cost where available.
Related
Book an AI agent testing review.
Bring your current test suite and a sample of production failures. We will propose one Agent Improvement Sprint.