Monitor AI agents with tracing and observability

Overview

Once an agent is in production, the hardest problems are often invisible: slow tool calls, brittle prompts, looping behavior, and confusing handoffs. This course teaches you how to instrument agents so you can trace execution and diagnose failures quickly.

Who it's for

• Developers running agent workflows in staging or production
• Platform teams responsible for AI reliability
• Product teams that need better visibility into agent behavior

What you'll build

• A tracing setup that records prompts, tool calls, outputs, and timing
• A dashboard for identifying slow paths, failure clusters, and retry loops
• A debugging workflow for reproducing and fixing agent incidents

Prerequisites

• An agent workflow with multiple steps or tool calls
• Access to logs or a staging environment
• Basic familiarity with monitoring concepts

Tools and setup

1. Decide which spans, events, and metadata matter most
2. Instrument prompts, tool invocations, and final outputs
3. Capture enough context to reproduce failures without leaking sensitive data

Modules

Module 1: Trace the execution path

You will record the agent's steps from user input to final action so you can understand what happened when something goes wrong.

Module 2: Build useful dashboards

You will track failure rates, tool latency, retry frequency, and cost so the team can spot unhealthy behavior early.

Module 3: Turn traces into fixes

You will use the collected traces to isolate brittle prompts, bad tool contracts, and ambiguous routing logic.

Deliverable

An observability setup that makes agent behavior measurable, debuggable, and easier to improve.

Common mistakes

• Logging final answers but not intermediate tool behavior
• Collecting too much raw data without defining the questions it should answer
• Ignoring privacy and retention concerns when storing traces

Next steps

Connect observability to incident reviews, eval pipelines, release gates, and model-routing decisions.