Tool Calling Is Not Just Plumbing for AI Agents — Roy Derks
AI Summary
Summary of Video Transcript on Tool Calling in AI Agents
Importance of Tool Calling
- Tool calling is crucial for AI agents, providing more than just support functions.
- It’s essential to build reusable, robust tools that can be integrated into various agent frameworks.
- There’s a growing recognition of the importance of tool platforms and libraries.
Agent Frameworks and Tools
- The focus has often been on improving agents, but not enough on the tools they use.
- Tools often break first, highlighting the need for better tool development.
- Tools should be designed to be flexible and adaptable to different frameworks.
Tool Definitions and Implementations
- Tools should have a simple name, a descriptive prompt, input parameters, and an output schema.
- The output schema is increasingly important for structured outputs and chaining tool calls.
- Tools can be defined dynamically using languages like GraphQL and SQL.
Traditional vs. Embedded Tool Calling
- Traditional tool calling involves back-and-forth communication between the client, server, and model.
- Embedded tool calling is a black-box approach where the agent handles all tool logic internally.
Separation of Concerns
- Developers should aim for a separation of concerns without overcomplicating the system.
- The Model Context Protocol (MCP) offers a way to separate the client and server sides of AI applications.
Standalone Tool Platforms
- Standalone tool platforms allow for the creation and execution of tools outside the agent framework.
- These platforms provide SDKs for integration and can handle tool chaining, authorization, and errors.
- They offer flexibility to switch between different agent frameworks.
Dynamic Tools
- Dynamic tools allow for the use of existing APIs and databases without creating numerous static tools.
- They enable models to generate queries like GraphQL based on a given schema.
- While offering flexibility, there are trade-offs with model accuracy and potential hallucinations.
Conclusion
- The effectiveness of AI agents is heavily dependent on the quality of their tools.
- As AI continues to advance, the development of tools should not be overlooked.
Notes
- No detailed instructions such as CLI commands, website URLs, or tips were provided in the transcript.
- The speaker, Roy, has a background in startups and has worked at IBM.
- He encourages connecting with him on social media for further information.