AI Agent Tracing

Every AI agent is a black box until you add tracing. See exactly what your agents are doing, why they’re failing, and how to make them better.

Tracing captures the complete execution flow of your AI agents—from the initial user request to the final response. Think of it as a flight recorder for your AI: when something goes wrong (or right), you have the complete story.

Tracing transforms AI development from guesswork to data-driven optimization. See every LLM call, tool execution, and decision your agents make.

Why AI Agents Need Tracing

AI agents are inherently complex—they make decisions, call tools, interact with LLMs, and chain operations together. Without tracing, you’re debugging in the dark.

🔍 Visibility - See Inside the Black Box

What you get:

Complete execution flow of every agent run
Every LLM call with exact prompts and responses
All tool executions and their results
Decision points and reasoning chains
Context flow between operations

Why it matters: Your agent might be failing because of a subtle prompt issue, a tool returning unexpected data, or a logic error in the chain. Tracing shows you exactly where and why.

🐛 Debugging - Fix Issues Fast

What you get:

Pinpoint exact failure locations in complex chains
See the exact inputs that caused errors
Track error propagation through your agent
Access complete stack traces and context
Compare successful vs failed executions

Why it matters: Instead of spending hours reproducing bugs, you see exactly what happened in production. Debug in minutes instead of days.

⚡ Optimization - Make Agents Faster & Better

What you get:

Execution time breakdown for every operation
Performance bottleneck identification
Input/output analysis for optimization opportunities
Error pattern detection
Prompt engineering insights

Why it matters: See which parts of your agent are slow or causing issues. Optimize prompts, reduce unnecessary operations, and improve performance with data, not guesswork.

📊 Monitoring - Keep Production Healthy

What you get:

Real-time performance metrics
Success/failure rate tracking
Resource usage monitoring
Alert integration for anomalies
Historical trend analysis

Why it matters: Know when your agents start failing before your users complain. Monitor performance trends and catch issues early.

⚠️

Production AI agents without tracing are a ticking time bomb. When they fail—and they will—you need complete visibility to fix them quickly.

What Handit.ai Tracing Captures

Every execution creates a complete trace with:

Data Type	What’s Captured	Example
Execution Flow	Complete operation sequence	User request → LLM reasoning → tool calls → final response
LLM Interactions	Prompts, responses, and timing	”Analyze this data…” → 1.2s → “The data shows…”
Tool Executions	Input parameters, outputs, errors	`search_web("AI agents")` → 10 results → 0.8s
Performance	Timing and resource usage	Total: 3.2s, LLM: 1.2s, Tools: 0.8s
Context	Data flow between operations	Search results → LLM analysis → structured output

AI Agent Tracing Dashboard

Tracing Components

🤖 Agent Tracing

The orchestrator - Tracks your entire agent from start to finish.

Captures the complete agent execution as a single trace, automatically linking all LLM calls, tool executions, and operations together. This is your agent’s “flight recorder.”

Best for: Main agent workflows, API endpoints, complex multi-step processes

What Agent Tracing Records

Complete execution timeline with start/end times
All child operations (LLM calls, tool executions) automatically linked
Success/failure status with detailed error context
Input/output data for the entire agent run
Performance metrics including total duration and resource usage
Error handling with retry attempts and recovery actions

Example Use Cases:

Customer support agent handling user queries
Research agent gathering and analyzing information
Code generation agent creating and testing solutions

🧠 LLM Node Tracing

The brain monitor - Tracks every interaction with language models.

Records complete details of LLM interactions including prompts, responses, and performance. Essential for understanding and optimizing your agent’s reasoning.

Best for: LLM calls, prompt engineering, performance optimization

What LLM Node Tracing Records

Complete prompts and responses with exact text
Model parameters (temperature, max_tokens, etc.)
Response timing and latency analysis
Error handling for failed LLM calls
Input/output data flow between operations

Example Use Cases:

GPT-4 calls for reasoning and analysis
Claude for document processing
Custom model endpoints
Embedding generation for RAG systems

🛠️ Tool Tracing

The action tracker - Monitors every tool and function your agent uses.

Captures detailed information about tool executions, from simple functions to complex API calls. See exactly what tools are called, with what parameters, and what they return.

Best for: Custom functions, API calls, database queries, file operations

What Tool Tracing Records

Function parameters and return values
Execution timing and performance metrics
Error handling with detailed error messages
Resource usage for expensive operations
Side effects like file writes or API calls
Retry logic and failure recovery

Example Use Cases:

Web search APIs for information gathering
Database queries for data retrieval
File processing and manipulation
External service integrations

Getting Started

Ready to see inside your AI agents? Add tracing in under 5 minutes and get complete visibility into your agent’s behavior.

Start tracing your agents today:

Quick Setup Guide

Transform your AI agents from mysterious black boxes to fully observable, debuggable, and optimizable systems.