AI Agent Tracing

Your autonomous engineer needs eyes to fix your AI. Tracing provides complete visibility into your AI agents so the autonomous engineer can detect issues, analyze root causes, and generate proven fixes.

Think of tracing as the diagnostic system that powers your autonomous engineer. When something goes wrong with your AI, tracing ensures your autonomous engineer has all the data needed to understand what happened and create an effective fix.

The Black Box Problem

AI agents are inherently complex systems. They make decisions, call tools, interact with language models, and chain operations together in sophisticated ways. When something goes wrong, figuring out what happened can feel impossible.

Picture this scenario: Your customer service AI suddenly starts giving unhelpful responses. Users are complaining, but you have no idea why. Did the language model change its behavior? Is a tool returning bad data? Is there a logic error in your agent’s reasoning chain? Without visibility into what’s actually happening, you’re reduced to guessing and hoping.

This is the reality for most AI teams. They build sophisticated agents but operate them blindly. When issues arise, debugging becomes a time-consuming process of reproduction attempts and educated guesses. Critical problems can persist for days while teams struggle to understand what’s going wrong.

Tracing solves this fundamental problem by capturing the complete execution flow of your AI agents. Every decision, every tool call, every LLM interaction, and every data transformation gets recorded with full context. Your autonomous engineer can see exactly what your AI is doing, why it’s making specific choices, and where problems occur.

How Tracing Powers Your Autonomous Engineer

Tracing data is essential for autonomous AI improvement. Without comprehensive visibility into your AI’s behavior, your autonomous engineer would be working blind—unable to detect issues or understand what changes might help.

Pattern Recognition: By analyzing thousands of traced interactions, your autonomous engineer identifies patterns that would be impossible to spot manually. Maybe your AI struggles when users ask complex multi-part questions, or perhaps it performs poorly during high-traffic periods. These insights drive targeted improvements.

Root Cause Analysis: When quality issues arise, your autonomous engineer uses trace data to understand exactly what’s going wrong. Instead of generic fixes, it can pinpoint whether problems stem from prompt issues, tool failures, or logic errors in your agent’s reasoning.

Fix Validation: Before creating pull requests, your autonomous engineer uses historical trace data to test potential improvements. It can see how proposed changes would have affected past interactions, ensuring fixes actually solve problems rather than creating new ones.

Continuous Learning: As your autonomous engineer’s fixes get deployed, new trace data shows their effectiveness. This feedback loop helps your autonomous engineer learn what types of improvements work best for your specific AI system.

What Gets Traced

Comprehensive tracing captures every aspect of your AI’s operation:

Complete Execution Flow: From the moment a user submits a request until your AI delivers a response, every step gets recorded. You can see the entire journey your request takes through your AI system.

LLM Interactions: Every call to language models gets captured with the exact prompts sent, complete responses received, token usage, timing data, and any errors that occurred. This visibility is crucial for understanding how your AI uses language models.

Tool Executions: When your AI calls external tools or functions, tracing captures the parameters passed, results returned, execution timing, and any error conditions. This helps identify when tools are causing problems or performing poorly.

Decision Points: Tracing shows why your AI made specific choices—which tools it decided to use, how it processed information, and what reasoning led to particular responses. This insight is invaluable for understanding your AI’s behavior.

Context Flow: See how information moves through your AI system—what context gets passed between operations, how data transforms as it flows through your agent, and where information might get lost or corrupted.

The Debugging Revolution

Tracing transforms debugging from a frustrating guessing game into a precise diagnostic process:

Instead of hours of reproduction attempts, you see exactly what happened in production. The trace shows the complete context and execution flow that led to any issue.

Instead of generic error messages, you get detailed information about what went wrong, where it happened, and what conditions led to the failure.

Instead of wondering about performance, you see precise timing data showing which operations are slow and where bottlenecks occur in your AI system.

Instead of guessing about user experience, you can analyze actual user interactions to understand how your AI performs in real-world scenarios.

Real-World Impact

Teams using comprehensive tracing report fundamental changes in how they manage AI systems:

Faster Issue Resolution: Problems that used to take days to diagnose and fix now get resolved in hours. The complete visibility into AI behavior eliminates the guesswork from debugging.

Proactive Improvement: Instead of waiting for user complaints, teams can identify performance issues and optimization opportunities by analyzing trace data patterns.

Confident Deployments: With detailed visibility into how changes affect AI behavior, teams can deploy improvements with confidence, knowing exactly what impact they’ll have.

Better User Experience: Understanding exactly how AI systems behave in production enables teams to optimize for real user needs rather than theoretical performance metrics.

Types of AI Systems That Benefit from Tracing

Tracing proves valuable across diverse AI applications:

Customer Service Agents: See how your AI handles different types of customer inquiries, which responses work best, and where conversation flows break down.

Content Generation Systems: Understand how your AI processes content requests, what sources it uses for information, and how it structures responses.

Decision-Making AI: Track how your AI evaluates options, what factors influence its decisions, and where its reasoning might be improved.

Multi-Step Workflows: For complex AI systems that chain multiple operations together, tracing shows how information flows between steps and where bottlenecks or failures occur.

Getting Started

Ready to give your autonomous engineer the visibility it needs to detect issues and create fixes?

Or learn more about tracing specifically:

Next Steps: Once tracing is active, set up Evaluation to score your AI’s performance and Autonomous Fixes to complete your autonomous engineer setup.