LLM Research

A tool-using AI agent usually fails in a very ordinary way. It does not announce a philosophical crisis. It calls the wrong tool, calls the right tool too many times, writes malformed code, searches before thinking, or confidently takes a useless action because the training process rewarded motion rather than judgment. This is the unglamorous part of agent deployment. The demo shows the agent booking, searching, calculating, and reporting. The training log shows wasted exploration, unstable optimization, and a strange habit of confusing “using tools” with “thinking better.” Apparently, giving a model a calculator does not automatically make it an accountant. Shocking. ...

A chatbot answers. An agent checks. That distinction sounds small until a workflow fails at 2:17 p.m. because the model confidently invented a policy clause, skipped the database lookup, and then explained itself with the serene authority of a consultant who has already left the building. The 2022 paper ReAct: Synergizing Reasoning and Acting in Language Models matters because it made that failure mode harder to ignore.1 It did not simply ask language models to “think step by step.” Chain-of-thought prompting already did that. It did not simply attach a search box to a model. Retrieval-augmented systems were already moving in that direction. The paper’s real contribution was more architectural: it showed that a language model could alternate between reasoning, acting, observing, and revising its next move. ...