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.

The Problem: AI Black Boxes

When your AI starts giving bad responses, 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 reasoning chain? Without visibility, you’re debugging blind.

Tracing solves this by capturing everything your AI does. Every decision, every tool call, every LLM interaction gets recorded with full context. Your autonomous engineer can see exactly what happened and create targeted fixes.

How Your Autonomous Engineer Uses Tracing

Issue Detection: By analyzing trace patterns, your autonomous engineer spots when quality drops, which interactions fail, and what causes problems.

Root Cause Analysis: Detailed trace data helps your autonomous engineer understand whether issues stem from prompt problems, tool failures, or logic errors.

Fix Validation: Before creating pull requests, your autonomous engineer tests improvements against historical trace data to ensure fixes actually work.

Continuous Learning: As fixes get deployed, new trace data shows their effectiveness, helping your autonomous engineer improve over time.

What Gets Traced

When you add tracing to your agent entry points, Handit automatically captures:

• Complete execution flow from user request to final response
• All LLM interactions with prompts, responses, and performance data
• Tool executions with parameters, results, and timing
• Error details with full context for debugging
• Performance metrics showing bottlenecks and optimization opportunities

Getting Started

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