RELIABILITY

Claude outages and 529 overloads expose whether your AI coding workflow has checkpoints, receipts, model-switch paths, and small enough task slices to survive provider degradation.

LangChain's rubrics for Deep Agents point at a practical agent pattern: self-correction works only when rubrics are versioned, executable, and sampled against human review.

A long-running coding agent is only useful if the environment around it can queue tasks, capture logs, checkpoint state, verify behavior, limit cost, and recover from failure.

The defensive patterns that keep Claude integrations alive in production. Retry shapes, backoff with jitter, circuit breakers, fallback chains, and the observability you need to debug at 3am.

The math of agent pipelines is brutal. 85% reliability per step compounds to about 20% at 10 steps. Here is why long chains collapse in production, and the six patterns the field has converged on to fight the decay.