The Core Execution Loop Behind Modern AI Agents
Agents can browse the web, write and execute code, coordinate with other agents and reason through multi-step problems autonomously. The demos are impressive. Last year, as I started using agents more and more, to the point that they are now a part of my day-to-day, I wanted to understand them more fundamentally. Before the planning strategies, the memory systems and the multi-agent orchestration, there's a core architectural pattern that everything else is built on. Lilian Weng's excellent breakdown of LLM-powered agents taxonomizes agents into planning, memory, and tool use. I want to go one level deeper, into the execution loop that sits beneath all three, because understanding that foundation makes everything else click.
Most LLM applications follow a predictable path - a user sends a prompt and the model returns a completion. Maybe you chain a few calls together - summarize, then extract, then format. The point is, you, the...
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