New to AI agent observability? Read these guides in order. Each one builds on the one before it, and you can stop whenever you have what you need.
Step 1: Understand the problem
What Is AI Agent Observability?
A plain-English definition, four documented failures, and what to log. Start here if the term is new to you.
Step 2: See what your agent actually did
How to Trace an AI Agent Step by Step
How to capture each model call and tool call as part of one trace, with an example you can reproduce.
Step 3: Find out whether it is any good
How to Evaluate AI Agents
A practical framework built from nine cases where an evaluation score was misleading, and a check you can run for each.
Step 4: Know where the money goes
How to Track LLM Token Usage Per User, Per Feature, and Per Agent
How to turn a single provider invoice into answers about which product, customer or team is spending what.
Step 5: Choose tools
Best AI Agent Observability Tools Compared, Priced, and Cross-Checked
The main commercial and open-source platforms side by side, with pricing checked against vendor pages.
Best Open Source AI Agent Observability Tools (Self-Hosted)
What changed after several well-known open-source projects were acquired, and what that means if you self-host.
LangChain Observability: Complete Tracing Setup (and What It Really Costs)
A framework-specific walkthrough, including the costs that only show up after you switch tracing on.
Step 6: Get ready for production
AI Agent Observability in Production: The 25-Point Readiness Checklist
A checklist built on a documented incident, for checking whether you could detect and stop a misbehaving agent in time.
Prefer to browse by topic?
- AI Agent Observability Basics
- Tracing & Debugging
- Evaluation & Quality Metrics
- Cost & Token Monitoring
- Tools & Platforms Comparison
- Open Source Solutions
- Framework-Specific Guides
- Production Best Practices
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