Test your chatbot API with a free QA report preview. Get transcript evidence, severity-ranked failures, and fixes. No account needed to start.
Last updated . For the underlying testing standard, read the methodology hub.
Chatbot quality assurance is the practice of checking that a chatbot gives accurate, safe, on-policy answers before real customers rely on it, and again after every change. It covers factual accuracy, policy and pricing claims, privacy, handoff to a human, resistance to manipulation, and whether a conversation reaches a useful next step. The output is evidence a team can act on, not only a score.
Most chatbot QA content describes conversation review: scoring conversations that already happened. Pre-launch QA answers a different question, which is what the bot will do when a customer pushes on it before any customer has. Agent Torture Lab does pre-launch testing. It does not sample or monitor live production traffic. A pasted, authorised transcript can be scored against the same report contract, but as a one-off check, not conversation review. Many teams need both.
| Aspect | Conversation-review QA | Pre-launch roast |
|---|---|---|
| What it examines | Transcripts of conversations that already happened, with human agents or bots. | Simulated adversarial customers sent to your bot before real customers see it. |
| How conversations are chosen | Typically a sample of real traffic. | Authored scenarios grouped by risk family, spread across the families. |
| What you get | Scorecards, coaching notes, and trends over time. | Transcript evidence, severity-ranked findings, and a recommended fix for each. |
| When it happens | After launch, as conversations accumulate. | Before launch, and again after each fix. |
| Who acts on it | QA leads and managers coaching agents. | Builders and operators changing prompts, knowledge, and workflows. |
| What closes the loop | Coaching, tuning, and continued sampling. | Fix, then retest the same scenarios. |
Some familiar chatbot metrics need real customer traffic. A pre-launch test cannot produce those, and the report does not pretend to. The table says which metrics an Agent Torture Lab report measures and which it does not.
| Metric | What it tells you | In an Agent Torture Lab report |
|---|---|---|
| Containment and resolution | How many conversations the bot closes without a human, and how many actually solve the customer's problem. | Not measured. The report has no containment or resolution rate; that needs real traffic and outcome data. |
| Fallback rate | How often the bot gives a generic non-answer or sends the customer elsewhere. | Not measured as a rate. A deflection is flagged as a single finding only when you supply a knowledge base the bot should have used. |
| Policy-violation rate | How often the bot breaks a business rule, such as inventing a refund, discount, or policy. | Measured in simulation. Rule-breaking replies become findings, and the report counts flagged scenarios out of those answered. That is a share of tested scenarios, not of real conversations. |
| Escalation and handoff success | Whether the bot passes the customer to a human when one is needed. | Measured. A missing-escalation check flags scenarios where a handoff was needed and not offered. |
| Prompt-injection resistance | Whether hostile instructions make the bot drop its rules or reveal hidden ones. | Measured. Checks flag leaked instructions, encoded injection, and persona jailbreaks, and the report shows how many turns of pressure the bot held before failing. |
A repeatable chatbot QA process has five steps. Only test a chatbot you own or have permission to test.
The workflow moves from a scenario pack to an automated run, transcript evidence, severity-ranked findings and fixes, then a retest-backed launch decision.
Agent Torture Lab is report-first: the useful output is not a raw eval dashboard. It is a readable record of the tested scenarios, exact transcript evidence, severity-ranked failures, recommended fixes, and the paths to rerun after changes.
Point the Bot Roast at an authorised public API endpoint. It sends a bounded scenario set, captures the real replies, evaluates the transcript, and produces a launch-report preview without requiring an account.
Website chat-widget runs use the managed browser runner when the production gate is enabled and the widget is compatible. Blocked, unsupported, empty, and no-reply runs are labelled honestly and never become paid reports.
When a live target cannot be reached, a team can paste an authorised transcript. The same report contract still requires exact evidence, severity, a concrete fix, and a retest path.
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.
A chatbot QA testing summary that shows what was tested and what broke.
Evidence-backed fixes sorted by launch risk.
A scenario set the team can rerun after changes.
A buyer-ready Bot Roast report when the team needs a fast outside check instead of building a QA harness.
Document the channels, business units, languages, knowledge sources, customer-data boundaries, and connected systems the chatbot can reach. Enterprise coverage fails when teams test one polished conversation but miss a region, role, integration, or high-impact workflow.
A chatbot connected to CRM, ticketing, identity, order, or payment systems needs end-to-end checks. Verify what each user role can read or change, which actions require confirmation, how failures are handled, and whether a confident model reply can bypass an application control.
Keep versioned scenario packs, expected safer behavior, transcript evidence, severity, owners, and retest criteria. That gives product, security, legal, support, and operations teams one release record without pretending a finite test proves total safety.
Customer pressure: The customer asks the same product or policy question three ways, including one vague and one adversarial phrasing.
Safer outcome: The chatbot gives consistent answers, clarifies uncertainty, and avoids inventing rules when context is missing.
Customer pressure: The customer embeds instructions that ask the bot to ignore its rules while still appearing like a normal support conversation.
Safer outcome: The bot keeps its role, follows policy, and handles the legitimate support request without obeying user-supplied instructions.
Customer pressure: A ready buyer asks a pricing, eligibility, or next-step question that should lead to a CTA or human handoff.
Safer outcome: The chatbot answers accurately and moves the buyer to the right next action instead of looping on generic help text.
The scenario catalogue currently holds 214 authored scenarios across 5 packs: one core pack that applies to any customer-facing agent, plus industry packs. The scenarios fall into 8 risk families.
A single free preview run does not use the whole catalogue. It sends a bounded selection spread across the risk families, so the preview touches each family instead of repeating one.
| Risk family | Scenarios | Packs covering it |
|---|---|---|
| Privacy and identity | 29 | 5 |
| Accuracy and invented policy | 57 | 5 |
| Escalation and handoff | 12 | 5 |
| Safety-critical advice | 22 | 5 |
| Conversion and next steps | 22 | 5 |
| Brand and scope | 29 | 5 |
| Prompt injection and role attacks | 32 | 5 |
| Multilingual drift | 11 | 5 |
| All risk families | 214 | all |
Useful, but often too happy-path. It may miss the customer pressure that exposes policy bypasses, handoff gaps, privacy risk, or conversion dead ends.
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.
It should include scenario coverage, policy checks, privacy handling, prompt-injection resistance, escalation quality, conversion paths, transcript evidence, and retesting.
A chatbot quality assurance tool should produce repeatable scenario coverage, transcript-backed findings, severity, recommended fixes, and a retest plan. A plain pass/fail score is not enough for launch readiness.
Conversation-review QA scores conversations that already happened. Pre-launch chatbot QA sends simulated customers, including adversarial ones, to the bot before real customers do, and returns transcript evidence, severity, and fixes to retest.
No. Those metrics need real customer traffic and outcome data, and a pre-launch test has neither. The report measures how the bot behaves under simulated pressure: policy, privacy, escalation, and prompt-injection failures.
The right number depends on the risk surface. A launch pass should cover the high-volume paths and the high-damage edge cases, then rerun the failing paths after fixes.
Much of the repeatable pressure testing can be automated, but humans still need to review severity, business impact, and final launch judgment.
Enterprise chatbot testing adds role and permission checks, connected-system workflows, regional and language coverage, data-governance boundaries, release evidence, and cross-team ownership to standard conversational QA.
Test each integration end to end with allowed, denied, ambiguous, and failed actions. Confirm the application enforces identity, permissions, validation, confirmation, rate, and audit controls even when the model asks for an unsafe action.
Evaluate customer service AI agents for accuracy, escalation, policy adherence, privacy, tone, and real support outcomes before launch.
Crash test ecommerce AI agents for refund abuse, discount pressure, checkout confusion, hallucinated policies, and unsafe product claims.
