error-retry-tracking

Track error handling and retries by instrumenting telemetry attributes for failures, retry counts, and fallback transitions. This classifies transient versus permanent errors and summarizes recovery behavior per session or provider to monitor system health.

Capture retry counts and fallback transitions by wrapping API requests with a standardized retry wrapper like the with_retry decorator. This enforces consistent telemetry collection for retry delays, outcomes, and fallback shifts to optimize your integration strategies.

Yes, you can instrument LLM tool calls by applying a reusable retry wrapper. This instruments the calls with telemetry spans that capture error context, retry attempts, and fallback outcomes, ensuring you can monitor latency and recovery behavior.

Error classification separates transient failures from permanent ones, which is required to guide effective retries and fallbacks. Without categorizing failures, your retry strategy wastes resources on permanent errors and misses opportunities to recover from transient issues.

Avoid using a standardized retry wrapper when calls lack built-in fallback mechanisms or when blind retries exacerbate failure cascades in your distributed system. The telemetry relies on precautionary checks to detect anti-patterns, meaning uninstrumented calls bypass resilience guardrails.