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Tool Execution & Results

Master invocation patterns, result handling, and error management for reliable AI agents

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Result Handling: From Data to Intelligence

A tool returns data—but an agent needs intelligence. Result handling is the art of transforming raw responses into actionable information the agent can reason about and act upon.

This process involves parsing, validation, extraction, and formatting—each step critical to agent reliability.

Interactive: Result Parsing Pipeline

Watch how raw tool output transforms into agent-ready data

Receive Response
Raw bytes from tool execution
HTTP 200 OK Content-Type: application/json{'{'status':'success','data':{...}}}

Result Format Comparison

Unprocessed tool response
{
  "status": "success",
  "timestamp": "2024-01-15T10:30:00Z",
  "data": {
    "temperature": 22,
    "humidity": 65,
    "conditions": "partly cloudy",
    "forecast": [...]
  },
  "metadata": {
    "source": "weather-api",
    "cached": false
  }
}
Complexity:
Low
Best Usage:
Developer debugging, full access
Agent-Ready Output
It's 22°C and partly cloudy in Tokyo.

Result Handling Best Practices

✅
Always Validate
Check response schema, types, and required fields before use
🔍
Extract Essentials
Pull only what the agent needs—avoid passing bloated objects
🏷️
Add Metadata
Include source, timestamp, confidence for traceability
📐
Normalize Format
Standardize structure across different tools for consistency
🛡️
Handle Nulls
Gracefully manage missing, null, or undefined values
📝
Log Raw Results
Keep original responses for debugging and auditing
←Invocation Patterns