From Brains to Agents: My Journey Building LLM Systems That Act

Author(s): Jofia Jose Prakash

Originally published on Towards AI.

How I moved from “answers” to “actions” with retrieval, tools, and agent loops

Large‑language models (LLMs) are born as generalist engines of static, albeit powerful, brains. Static in the sense that while their outputs can be magical, they are capped at a knowledge cutoff baked in at training time. This is a fact of life that any developer leveraging these models quickly encounters when asking them about anything beyond their training: current events, the latest internal policies, or proprietary information not in their pretraining data. This recognition is what led me (and many engineers like me) down the rabbit hole of what I would consider the three big evolutionary steps to elevating LLMs from mere answering machines to active partners: retrieval‑augmented generation (RAG), tool calling, and autonomous agents. In this article, I share my insights that I’ve gleaned on why these three things are important, and the shifting paradigms that result from each.

The article discusses the evolution of large-language models (LLMs) from static tools to dynamic agents capable of performing a range of tasks. It explores three key developments: retrieval-augmented generation (RAG) that enhances LLMs by providing real-time, relevant data, tool calling that enables models to interact and perform external actions, and autonomous agents that can observe, plan, and act with varying degrees of complexity. Each level enhances user interaction and capabilities while introducing new challenges and considerations, particularly regarding security and the need for responsible AI development.

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Published via Towards AI