Observability and Debugging
P1Depth:
Instrumentation patterns for understanding agent behavior through logging, distributed tracing, metrics collection, error propagation analysis, and performance profiling.
Harness Layers
Meta
Meta (principles / narrative / research)
Prompt
Prompt (templates / few-shot / system instructions)
Orchestration
Orchestration (chaining / routing / looping)*
Integration
Integration (tools / RAG / external APIs)
Guardrails
Guardrails (output validation / safety checks)
Memory
Memory (context / state / persistence)*
Eval
Eval (testing / metrics / iteration)*
3 of 7 layers covered
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Recommended entry points for exploring this thread.
Recommended start
Research Report 7.3: Observability & Debugging
Traditional monitoring tells you a server is down - LLM observability must tell you that your agent is confidently generating wrong answers and nobody noticed
Eval
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
Eval
ACE Comprehensive Reference Specification
The unified framework that production-grade agent platforms use to make context work at scale
MemoryMemoryEval