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In this talk, we will demo how to use the LangChain Python package to build custom tools and leverage pre-built LangChain tools for an AI agent. This talk will showcase an AI agent’s ability to differentiate appropriate use cases for each tool. We’ll explore the end-to-end process from setting up the environment to generating responses with the AI agent. The use cases, benefits, and drawbacks of agentic frameworks will all be covered in this talk to provide a comprehensive understanding of AI agents to audience members of all experience levels.
By the end of this talk, you’ll be able to: * Distinguish agentic and non-agentic systems and their reasoning paradigms. * Build your own AI agent with custom LangChain tools that meets the needs of your task-oriented AI system. * Import and utilize pre-built LangChain tools to fit your workflow.
