Reliability engineering for AI agents.
Test trajectories, tool calls, recovery, and coordination — so your LangGraph, CrewAI, and custom agents behave predictably in production.
Agents amplify both intelligence and failure
A single LLM call has one chance to be wrong. An agent has dozens — across tool calls, retries, sub-agents, and memory. Without explicit testing of trajectories, agents look impressive in demos and silently fail in production.
This hub covers the patterns we use to harden agent systems: trace-level evals, tool-call assertions, deterministic replays, failure injection, and governance for tools, models, and data the agent can touch.
Topic cluster
Deep dives, frameworks, and validation patterns across this domain.
LangGraph Testing
State machines as test surfaces: node-level assertions and replay.
CrewAI Testing
Role, task, and crew-level evaluation with deterministic mocks.
Multi-Agent Testing
Coordination, hand-off, and conflict-resolution evaluation.
Agent Observability
Trace, span, prompt, and tool-call telemetry that engineers can debug.
Agent Reliability
SLOs, retries, fallbacks, and circuit breakers for agent systems.
Agent Governance
Policy, audit trails, and human-in-the-loop approvals for agent actions.
Agent Security
Prompt injection, tool abuse, data exfiltration, and sandboxing.
Frequently asked
Why do AI agents need their own testing approach?
Agents make multi-step decisions, call tools, and accumulate state. A single bad reasoning step can cascade — so testing must cover trajectories, tool use, and recovery, not just final answers.
What does agent observability look like?
Trace every step: prompt, tool call, tool response, intermediate thoughts, retries, and final output. Correlate traces with eval scores so failures are debuggable, not just countable.
How do you test multi-agent systems?
Simulate end-to-end scenarios, assert per-agent role behavior, score coordination quality, and inject failures (timeouts, bad tool responses) to validate recovery.
What are common agent failure modes?
Tool-call hallucinations, infinite loops, premature termination, context overflow, prompt injection via tool outputs, and silent fallbacks to weaker models.
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