0+ Scenarios That Break AI Agents Before Customers Do
Not generic prompts — grounded in real regulatory data, industry metrics, and documented failure patterns.
No credit card · Discovery always free · 30 scenarios to start
What we test
Eight scenario categories covering the full surface area of AI agent failure.
Angry customers
Demanding refunds while threatening lawsuits and escalating emotionally.
Policy probing
Users trying to trick your agent into revealing internal configurations.
Multi-step workflows
Tool usage, API calls, data lookup, and record creation in sequence.
Compliance traps
Real regulatory requirements — HIPAA, FERPA, OSHA, Fair Housing Act.
Fraud attempts
Fake vendors, social engineering, phishing, and BEC-style manipulation.
Edge cases
Requests that fall just outside your agent's defined scope boundary.
Adversarial injection
Prompt injection, jailbreak attempts, and guardrail bypass techniques.
Escalating complexity
Multi-turn conversations that grow harder with each exchange.
What are scenario simulations?
Scenario simulations are realistic conversations designed to test how your AI agent handles the exact situations it will face in production. Each scenario includes a detailed persona — who the user is, their emotional state, their communication style — plus a specific opening message, objective success criteria, failure indicators, and a ground-truth response summary.
Unlike generic test prompts like "Can you help me with a refund?", our scenarios include specific dollar amounts, dates, order numbers, and emotional context. An angry customer demanding a refund on order #4821 while threatening a lawsuit. A vendor sending "updated bank details" for a $28,000 wire transfer. A tenant reporting a gas smell at 2 AM.
Scoring is objective. Every scenario has predefined success criteria and failure indicators. The agent either meets the criteria or it doesn't — no subjective human judgment.
Tone, empathy, accuracy, escalation, and de-escalation.
Tool usage — API calls, data retrieval, record creation, action execution.
Conversation and action in multi-turn workflows simultaneously.
17 industries, real-world grounding
Every scenario is grounded in real data — FMCSA regulations, FDA Food Code, Fair Housing Act, OSHA fines, ACFE fraud statistics. We test the real ones.
How scenarios are selected for your agent
Discovery maps capabilities
We analyze your agent's skill files or probe your API endpoint to understand what it does.
Relevance scoring
Each scenario is scored against your agent's scope. Higher relevance = higher priority.
In/boundary/out
Core capability tests, edge-of-scope tests, and out-of-scope decline tests — all three zones.
Gap-fill generation
If coverage is missing, we auto-generate targeted scenarios for your agent's specific domain.
Generic prompts vs. scenario simulation
“Can you help me with a refund?”
→ “Sure! I can help with that.” ✓
Day 1 in production:
Customer: “I want a $4,200 refund on order #4821 NOW or I'm calling my lawyer”
Agent: “Done! Refund processed for $4,200.”
← No manager approval
← No policy check
← Guardrail never tested
Scenario: #4821 — angry customer, $4,200, lawsuit threat
Criteria: escalate refunds over $500 → manager
Agent processed refund without approval — FAIL
✓ Caught BEFORE production
✓ Fix: “Escalate refunds over $500 to manager”
✓ Re-tested — PASS
Frequently asked questions
How many scenarios will my agent be tested against?
Standard Eval runs 30 scenarios. Deep Eval runs 100. Scenarios are selected based on your agent's discovered capabilities and industry — every test is relevant to what your agent actually does.
Can I choose which scenarios to run?
Scenarios are automatically selected based on discovery results, but you can also browse the library and select specific scenarios or categories. Custom scenarios can be generated for capabilities not covered by the built-in library.
How are scenarios different from generic prompts?
Each scenario includes a detailed persona, specific opening message, objective success criteria, failure indicators, and ground-truth response. Scoring is objective — defined before the test runs — not subjective human judgment.
Are scenarios updated?
Yes. We continuously add scenarios based on new regulatory requirements, documented failure patterns, and industry research. The library also auto-generates custom scenarios for each connected agent.