n AI agent can return HTTP 200, remain within its latency target, generate no infrastructure errors—and still make the wrong decision.
It can retrieve the wrong evidence, choose the wrong tool, carry stale state forward, violate a policy constraint, follow an unnecessarily expensive trajectory, or deliver the wrong business outcome while every conventional operations dashboard remains green.
Agentic Observability for AI Agents develops an operating model for that problem.
The book moves beyond conventional logs, metrics, traces, and dashboards to introduce a runtime evidence architecture that connects what an AI agent did with whether its behavior was correct, reliable, economical, safe, and authorized.
Its central progression is:
from observability of AI agents to agentic observability.
Instead of treating telemetry as something humans examine only after an incident, the architecture developed in this book supports a governed operational loop:
Evidence → Reasoning → Recommendation → Authority → Action → New Evidence
At the center of that model is the Observability Operations Agent—a bounded operational agent that can correlate evidence, reconstruct executions, compare hypotheses, request approved diagnostics, and recommend next actions.
But reasoning does not automatically grant authority.
The book therefore introduces a Governed Action Boundary that separates machine reasoning from permissions, policy, runbooks, approvals, and consequential action.
The innovation is not simply to use another AI agent to monitor AI agents.
It is to design an operational architecture in which runtime evidence, machine reasoning, operational authority, and action remain explicitly separated and governed.
What the Book Covers
Across 21 chapters, you will learn how to:
- recognize silent AI failures where infrastructure remains healthy while the business outcome is wrong;
- expand conventional observability with quality and cost as first-class production signals;
- understand the evidence boundaries across models, retrieval, tools, orchestration, memory, state, humans, and external systems;
- assess AI observability maturity by what a system can actually explain and prove;
- use OpenTelemetry as a portable evidence substrate for GenAI applications;
- model LLM calls as observable operations with appropriate runtime evidence;
- trace RAG pipelines through retrieval, reranking, provenance, context assembly, and generation;
- reconstruct complete agent trajectories, including tools, MCP interactions, retries, handoffs, delegation, memory, and state;
- observe multimodal pipelines where different modalities introduce different evidence boundaries;
- balance sampling, privacy, retention, and telemetry cost;
- design evaluation systems using deterministic checks, model-based evaluation, human judgment, golden datasets, and trajectory evaluation;
- connect evaluation directly to production telemetry;
- use replay, shadow evaluation, regression suites, canaries, and CI/CD quality gates;
- detect hallucination, drift, degradation, and changing behavior;
- observe safety controls, guardrails, prompt injection, abuse signals, and excessive agency;
- preserve a defensible regulated telemetry trail with provenance and accountability;
- understand token economics and cost attribution across models, retrieval, tools, retries, workflows, human intervention, and business outcomes;
- engineer latency and performance across complete AI-agent journeys;
- define SLOs for probabilistic systems where reliability includes quality and outcomes rather than uptime alone;
- investigate incidents through evidence chains, replay, trajectory comparison, and hypothesis testing;
- turn production failures into new evaluation cases, regression controls, and a governed learning loop.
Recurring Production Use Case
Rather than switching between unrelated toy examples, the book follows AsterVale Group, a recurring fictional travel, mobility, and service enterprise.
Its AI environment evolves across the entire book.
Readers see the same production landscape mature through:
RAG → agents → tools → memory and state → evaluation → safety → economics → reliability → incidents → governed operations
This allows the book to demonstrate something isolated examples usually miss:
observability capability accumulates.
The evidence captured in one stage becomes the basis for better evaluation, diagnosis, governance, and operational decisions later.
Who This Book Is For
This book is written for:
- AI and GenAI engineers
- AI-agent engineers
- ML engineers operating production systems
- AI platform engineers
- software and solution architects
- SRE and reliability engineers working with AI workloads
- observability and platform teams
- engineering managers and technical leaders
- teams designing production governance for increasingly autonomous AI systems
It assumes familiarity with modern software systems but does not require expertise in any particular observability vendor.
What Makes This Book Different
I would include this explicitly on Leanpub because it is a strong sales differentiator:
This is not another LLM tracing guide.
The book treats tracing as one part of a much larger production architecture.
Its distinctive ideas include:
- Observability of agents → Agentic observability
- Runtime evidence architecture
- Evaluation as operational telemetry
- Quality and cost as production signals
- Evidence connected to business outcomes
- Probabilistic reliability and AI-specific SLOs
- Observability Operations Agent
- Governed Action Boundary
- Evidence → Reasoning → Recommendation → Authority → Action → New Evidence
- A continuous production feedback loop that turns incidents into durable evaluation and regression assets
