How I Auto Track AI Agent Actions and Token Usage (n8n tutorial)
AI Summary
LLM Observability in AI Agents
Importance of Monitoring AI Agents
- AI agents are unpredictable and can hallucinate.
- Monitoring their usage is crucial for cost management and understanding actions taken.
System Overview
- The video demonstrates a template to monitor agent actions, token usage, and associated costs.
- A demo is presented, showing how logs are captured during agent actions.
Key Features
- Logging Actions:
- Records actions taken by the agent, including tool calls, tokens used, and costs.
- Example actions include retrieving contact information and sending emails.
- Error Handling:
- Logs error messages separately if the agent fails to perform an action.
- Customizable logging fields available.
- Intermediate Steps Option:
- Enabling “return immediate steps” helps track actions and parameters.
- Error Workflow Configuration:
- Settings can be adjusted to continue processing even after errors arise, allowing for alternative paths in workflows.
Technical Implementation
- JSON structures are utilized for logging.
- Google Sheets can be integrated for analytics and logging.
- Code examples are provided for controlling logging format and content.
Resources:**
- A JSON template for the logging system is available for download.
- Instructions provided for integrating with Google Sheets for data logging.
Conclusion
- The approach emphasizes systematic monitoring and error handling in AI agent workflows, important for managing costs and enhancing control over AI actions.
Note: Further resources and discussion available in the community links provided in the video description.