resilience-analysis

Tracing error propagation in an LLM agent framework involves mapping how exceptions move through the agent loop during tool failures, API errors, and state update failures. This analysis identifies critical containment gaps to ensure production readiness.

Adding circuit breakers and fallbacks requires cataloging recovery patterns for your agent's risky operations. You map failure modes for tools and data sources, then document and validate retry logic and fallback strategies to contain unsafe operations.

Sandboxing in LLM-driven agents is an isolation strategy to contain unsafe operations during execution. You need sandboxing when deploying tools or data sources that risk system instability, ensuring failures are contained without crashing the agent loop.

Your agent framework needs resilience testing before production deployment to verify it can handle tool failures, API errors, and state update failures. Running a resilience analysis generates a report highlighting critical gaps and hardening steps.

Evaluating an agent framework's recovery patterns involves running a resilience analysis against the target framework to map failure modes. This approach documents and validates retry logic, fallbacks, and circuit breakers to confirm containment and recovery.