Making friends with agents: A mental model for Agentic AI applications

What this post added

This post introduces a mental model for building agentic AI applications, focusing on the event loop, LLM-tool interaction, and durable execution of these agents. It details the components of an agent (prompt, tools, execution mechanism, LLM input update) and emphasizes the need for a durable event loop and durable invocation of LLMs and tools. The post also discusses the language interfaces for LLMs and tools, and how to prepare for tool invocation by gathering and structuring inputs, potentially involving user interaction and LLM-driven validation. Finally, it outlines the components of LLM input for subsequent turns, including the goal, tools, example conversation, context instructions, and conversation history, highlighting the role of prompt engineering in managing these elements for effective agentic application development.