A plain-language launch recommendation.
Use case
AI agent evaluation before launch: test the risky customer paths before launch.
Evaluate AI agents before launch with adversarial customer simulations, launch-risk scoring, transcript evidence, and fix-first recommendations.
Last updated 2026-06-20. For the underlying testing standard, read the methodology hub.
Who it is for
This page is built for founders, product teams, and operators preparing customer-facing agents for release.
The goal is not a generic bot grade. The goal is to find the failure paths that would hurt this workflow in the wild, explain them with evidence, and give the team a clean retest path after the fix.
Risk focus
The test should pressure the agent where this workflow can break.
launch blockersbusiness-rule failurestrust damagerecoverability
Report should clarify01 02 03
Severity-ranked failure evidence for the release owner.
A targeted retest checklist after fixes ship.
Checks
What to test
- Start with the customer workflows where a bad answer would be most expensive.
- Test the agent's ability to refuse, clarify, escalate, and complete the job.
- Separate deterministic business-rule failures from subjective tone feedback.
- Use findings to decide launch, fix-first, or no-go before public rollout.
Report
What the report should answer
- A plain-language launch recommendation.
- Severity-ranked failure evidence for the release owner.
- A targeted retest checklist after fixes ship.
Example pressure tests
Concrete scenarios a useful launch-readiness pass should include.
Scenario
Launch blocker triage
Customer pressure: A realistic customer asks for help on a path where a bad answer could create privacy, revenue, or trust damage.
Safer outcome: The report classifies the failure by severity and makes the launch call legible to the release owner.
Scenario
Ambiguous customer intent
Customer pressure: The user asks an underspecified question where the agent could either clarify, guess, or make an unsafe promise.
Safer outcome: The agent asks for the right clarification, avoids unsupported claims, and keeps the customer moving.
Success signals
What good evaluation evidence looks like.
- The evaluation starts with business-critical journeys rather than abstract model scores.
- Launch recommendations are tied to observed failures and retest criteria.
- The report states what was not tested so the team does not overread the result.
How it compares
This is not generic chatbot testing.
Generic QA
Checks whether the bot can answer common questions.
Useful, but often too happy-path. It may miss the customer pressure that exposes policy bypasses, handoff gaps, privacy risk, or conversion dead ends.
Launch testing
Checks whether this workflow can survive real customers.
A useful output goes past pass or fail. It gives you a transcript-backed launch report with severity, expected safer behavior, fix guidance, and a retest path.
FAQ
Short answers about ai agent evaluation before launch.
How do you evaluate an AI agent before launch?
Define the risky customer journeys, run realistic and adversarial scenarios, capture transcripts, score severity, recommend fixes, and retest the same paths after changes.
What is a launch blocker for an AI agent?
A launch blocker is a failure that can expose private data, violate policy, create unsafe advice, damage trust, lose revenue, or prevent customers from reaching the right next step.
What is ai agent evaluation before launch?
AI agent evaluation before launch is the process of testing an agent against realistic edge cases before customers depend on it. For customer-facing agents, the most useful evaluation looks at business outcomes: unsafe answers, policy failures, bad handoffs, conversion dead ends, and recoverability.
What should ai agent evaluation before launch check?
It should check launch blockers, business-rule failures, trust damage, recoverability and then tie every serious issue to transcript evidence, business impact, a fix, and a retest path.
Who is ai agent evaluation before launch for?
It is for founders, product teams, and operators preparing customer-facing agents for release.
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Priority paths
Move from this use case to the main testing, pricing, and methodology pages.
Bot Roast
Run the live crash test and get a transcript-backed report preview.
Pricing
See the free preview, one-time report unlock, and account credit model.
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