An opinion about MCP
The speaker is discussing the limitations and potential pitfalls of using specific frameworks or libraries for Agentic AI, particularly MCP (Multi-Client Platform). They highlight several issues:
- Uncertainty: The future of Agentic AI is uncertain, and new developments may render existing frameworks obsolete.
- Perspective adoption: When using a framework, you’re not just adopting its utility functions but also its underlying perspective on how the world works. This can lead to difficulties if that perspective changes or proves incorrect.
- Customization: Some frameworks assume certain communication patterns or agent interactions, which may not be universally applicable. If these assumptions don’t match your needs, heavy customization is required.
- Commitment risk: Adopting a specific framework can lead to commitment risks, making it harder to switch to alternative perspectives or frameworks as the field evolves.
The speaker emphasizes the importance of keeping flexibility and being open to new ideas and perspectives in the fast-evolving landscape of Agentic AI. They caution against teaching specific frameworks or libraries, such as Autogen, Small Agent, or Land Chain Graph, for now, citing the potential for them to become outdated quickly.
Instead, they suggest focusing on critical thinking and first principles, encouraging learners to challenge their assumptions about best practices in the context of AI development. They also highlight the importance of unlearning existing knowledge and biases, which can hinder one’s ability to adapt to new ideas and perspectives.
Translation
Reference:
https://www.youtube.com/watch?v=kwwjR6HHJPM https://yage.ai/mcp.html