Agentic Observability for AI Agents

About this book

AI agents need more than monitoring. They need an operating system for evidence, evaluation, reliability and governed action.

Traditional observability answers questions such as Is the service available?, Where did the request fail? and Why is latency increasing? This book moves beyond logs, metrics, traces and dashboards to a runtime evidence architecture that connects what an AI agent did with whether that behaviour was acceptable, reliable, economical, safe and authorised.

Its central innovation is the progression from observability of agents to agentic observability: an Observability Operations Agent that correlates evidence, investigates failures, compares hypotheses, requests approved diagnostics and recommends actions, while staying inside an explicit governed action boundary.

evidence → reasoning → recommendation → authority → action → new evidence

Inside the book, you will learn how to

• Observe the full GenAI architecture across models, RAG, tools, agents, memory, state and human intervention
• Use OpenTelemetry as a portable evidence substrate while governing fast-changing GenAI semantic conventions
• Trace RAG pipelines end to end, and reconstruct agent trajectories across tool calls, MCP, delegation, handoffs and retries
• Balance sampling, privacy, retention and telemetry cost without destroying diagnostic evidence
• Design an evaluation architecture spanning deterministic checks, model-graded evaluation, human judgement and trajectory evaluation, and wire results into runtime telemetry
• Detect hallucination, drift and degradation before aggregate health metrics hide the problem
• Observe safety, guardrails, prompt injection and excessive agency without confusing detection with action authority
• Attribute token and cost economics across models, tools, retrieval, retries and agents
• Debug GenAI incidents through evidence chains, trajectory comparison, replay and hypothesis testing
• Close the loop from production evidence to new evaluation cases and regression protection

AsterVale Group provides the recurring production use case, and a companion GitHub package adds exercises, synthetic traces, evaluation evidence, scorecards, incident bundles and worked solutions.

The harder question this book answers: how do we know what an AI agent actually did, whether it should have done it, what evidence supports that conclusion, and what action can safely follow?