Distributed Tracing
Trace complex agent workflows across LLM calls, tool executions, and sub-agent spawning.
Agent Observability: Debug Complex AI Systems
Agent Observability: Debug Complex AI Systems
Section titled “Agent Observability: Debug Complex AI Systems”AI agents are black boxes until you instrument them. This track teaches you to build observability into your agents from day one—tracing, logging, and monitoring that scales from prototype to production.
🎯 What You’ll Master
Section titled “🎯 What You’ll Master”Loop Detection
Identify and break infinite loops before they burn through your API budget.
Multi-Agent Debugging
Debug orchestration issues in multi-agent systems with clear visibility into agent communication.
Production Monitoring
Set up dashboards, alerts, and SLOs for AI systems in production.
🔍 Tracing & Logging
Section titled “🔍 Tracing & Logging” Agent Traces: The Complete Guide End-to-end tracing
LLM Call Logging What to capture
Tool Execution Traces Debug tool calls
Memory & State Tracking Track agent memory
🔄 Debugging Complex Behaviors
Section titled “🔄 Debugging Complex Behaviors” Loop Detection & Breaking Stop infinite loops
Multi-Agent Debugging Orchestration visibility
Error Classification Categorize failures
Root Cause Tracing Find the source
📊 Production Monitoring
Section titled “📊 Production Monitoring” Agent Dashboards Key metrics to track
Alerting Strategies When to page
SLOs for AI Systems Reliability targets
Observability Platforms Tool comparison
🚀 Quick Wins
Section titled “🚀 Quick Wins”- Add span tracing → See agent execution flow immediately
- Log token counts → Track cost in real-time
- Set loop limits → Prevent runaway agents
- Add latency metrics → Identify slow components
Coming Soon: Interactive Trace Visualizer
Our trace visualization playground is under development.