Research Report 6.4: Error Propagation & Resilience
If each step in your AI pipeline is 90% accurate, a ten-step chain drops to 35% reliability - and most teams don't realize this until production
Research into how errors cascade through LLM orchestration systems, the mechanisms for detecting and containing failures, and the architectural patterns that enable graceful degradation - covering circuit breakers, bulkheads, retry strategies, and chaos engineering for AI systems.
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Documentation on claude 20 research report 6.4! error propagation & resilience
Traditional monitoring tells you a server is down - LLM observability must tell you that your agent is confidently generating wrong answers and nobody noticed
The unified framework that production-grade agent platforms use to make context work at scale
Documentation on claude 22 research report 7.2! performance & optimization
Documentation on claude 23 research report 7.3! observability & debugging