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Everything about AI Function Calling (MCP), the keyword for Agentic AI
- The article discusses AI Function Calling and the Agentic AI framework specialized in this area, emphasizing the importance of function schemas for accurate implementation.
- It compares OpenAPI and Model Context Protocol (MCP) for function calling, noting the differences in JSON schema specifications and ecosystem sizes.
- The role of compiler-driven development in generating AI function schemas automatically is highlighted, reducing errors and improving efficiency.
- Document-driven development is crucial for creating reliable AI agents through detailed function documentation and descriptions.
- The orchestration strategy involves selecting candidate functions to reduce context and validation feedback for correcting argument errors.
- The Describer agent helps describe return values in markdown content or UI components, enhancing visualization in function calling.
- Agentica, the Agentic AI framework, utilizes LLM function calling and offers flexibility, scalability, and productivity in developing agents.
- The framework supports TypeScript Class, Swagger/OpenAPI Document, and MCP for efficient agent development.
- Overall, the article emphasizes the significance of accurate function schemas, documentation, and orchestration in successful AI function calling and agent development.
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