Break the tools.
Test the agent.
Inject production-style failures into an AI agent workflow. Replay every decision and measure whether recovery logic actually works.
scenarios
strategies
runs
Design an incident.
Watch the agent respond.
The same seeded episodes run against a naive baseline and your selected recovery strategy, making every comparison reproducible.
Adaptive recovery
Decision trace
Built to test behavior,
not decorate a chatbot.
Scenario layer
Describes target tools, fault type, probability, and severity with deterministic seeded sampling.
- 6 incident presets
- Compound faults
- Reproducible seeds
Fault proxy
Intercepts tool calls and returns realistic transport, contract, freshness, or side-effect failures.
- Timeout + 429
- Schema + stale data
- Partial writes
Agent strategies
Runs naive, guarded, and adaptive policies through identical workflows and incident samples.
- Bounded retries
- Validation + fallback
- Write verification
Evaluation
Aggregates traces into reliability metrics and preserves a representative replay for inspection.
- Success + recovery
- Latency + efficiency
- JSON evidence export
Tool-using agents fail at system boundaries: unreliable APIs, ambiguous writes, stale retrieval, and silent contract changes. This lab makes those risks visible before production.
Demonstrates agent architecture, eval design, observability, fault modeling, deterministic testing, and product communication in one inspectable build.
Replace the simulated proxy with live HTTP interception and emit OpenTelemetry spans into a production trace backend.