Tracing Features Overview

Transform your AI agents from black boxes to fully observable systems. Handit.ai’s tracing features give you complete visibility into every operation, from the highest-level agent workflow to individual LLM calls and tool executions.

Handit.ai provides three core tracing capabilities that work together to give you comprehensive observability:

• 🤖 Agent Tracing - The orchestrator that tracks your entire agent workflow from start to finish
• 🧠 LLM Node Tracing - The brain monitor that captures every AI model interaction with complete context
• 🛠️ Tool Tracing - The action tracker that monitors every function and tool execution

The Three Pillars of AI Agent Tracing

🤖 Agent Tracing: The Complete Picture

What it does: Wraps your entire agent workflow, creating the root trace that connects all operations together.

Perfect for: Main agent functions, API endpoints, end-to-end monitoring, production workflows

Key capabilities:

• Automatic operation linking - All child operations are automatically connected
• Complete execution timeline - From first input to final output
• Error propagation tracking - See how errors flow through your system
• Performance overview - Total execution time and resource usage

@tracker.start_agent_tracing()
async def process_customer_request(request):
    # Everything inside here is automatically traced
    intent = await classify_intent(request)
    context = await search_knowledge_base(intent)
    response = await generate_response(context)
    return response

const processCustomerRequest = startAgentTracing(async (request) => {Add commentMore actions
    // Everything inside here is automatically traced
    const intent = await classifyIntent(request);
    const context = await searchKnowledgeBase(intent);
    const response = await generateResponse(context);
    return response;
});

🧠 LLM Node Tracing: Understanding AI Decisions

What it does: Captures every interaction with language models, including prompts, responses, and performance metrics.

Perfect for: GPT/Claude calls, prompt engineering, token usage monitoring, model performance analysis

Key capabilities:

• Complete prompt history - Every prompt and response is recorded
• Performance metrics - Response time, token usage, and costs
• Model parameters - Temperature, max tokens, and other settings
• Error handling - Failed calls and retry attempts

@tracker.trace_agent_node("intent-classifier")
async def classify_intent(user_message):
    # LLM call is automatically captured with full contextAdd commentMore actions
    response = await llm.generate(f"Classify intent: {user_message}")
    return response

const classifyIntent = traceAgentNode({
    agentNodeId: "intent-classifier",
    callback: async (userMessage) => {
        // LLM call is automatically captured with full context
        const response = await llm.generate(`Classify intent: ${userMessage}`);
        return response;
    }
});

🛠️ Tool Tracing: Monitoring Actions

What it does: Tracks every tool execution, from simple functions to complex API calls and database operations.

Perfect for: Custom functions, external API integrations, database queries, file processing

Key capabilities:

• Function monitoring - Input parameters and return values
• Execution timing - How long each operation takes
• Error tracking - Detailed error context and stack traces
• Resource usage - Memory, CPU, and network utilization

@tracker.trace_agent_node("knowledge-search")
async def search_knowledge_base(query):
    # Tool execution is captured with timing and results
    results = await vector_db.similarity_search(query)
    return results

const searchKnowledgeBase = traceAgentNode({
    agentNodeId: "knowledge-search",
    callback: async (query) => {
        // Tool execution is captured with timing and results
        const results = await vectorDb.similaritySearch(query);
        return results;Add commentMore actions
    }
});

How They Work Together

The three tracing types create a complete observability stack:

Example trace hierarchy:

🤖 Customer Support Agent (2.3s)
├── 🧠 Intent Classification (0.8s)
├── 🛠️ Knowledge Search (0.9s)
├── 🧠 Response Generation (0.6s)
└── 🛠️ Response Validation (0.1s)

Why This Matters

🔍 Complete Visibility - See every operation, understand execution flow, track data transformations

🐛 Faster Debugging - Pinpoint exact failure locations, access complete error context, reduce debugging time from hours to minutes

⚡ Performance Optimization - Identify bottlenecks, track resource usage, optimize prompts and model parameters

📊 Production Monitoring - Monitor agent health, track success rates, get alerts for anomalies

Getting Started

Explore Each Feature

Ready to dive deeper into each tracing capability?

• Learn about Agent Tracing for end-to-end workflow monitoring
• Explore LLM Node Tracing for AI model interaction tracking
• Check Tool Tracing for function and tool execution monitoring

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