A chatbot answers in one shot. An AI agent runs in a loop, uses tools, and acts — Thought → Action →...
AI agents loop through Thought-Action-Observation cycles, using tool calls to overcome LLM constraints on calculation and current data. This shifts governance focus to tool design, iteration bounds, and approval gates—critical for production AI systems.
A chatbot answers in one shot. An AI agent runs in a loop, uses tools, and acts — Thought → Action → Observation → repeat — until the job's done. Watch one solve a multi-step task by calling a calculator and a search.
Agent = LLM + tools + a loop
You describe tools to the model (name, purpose, arguments). It can't divide big numbers reliably or know today's data — but it CAN decide "call the calculator with this expression". Tools cover the model's weak spots.
The ReAct loop
while (true) {
A practical explainer on the agent loop, how it works, why it is different from a chatbot, and where human-in-the-loop controls…
Everything you have built so far is reactive. User sends a message. System processes it. System...
State, stopping, and context — three engineering problems hiding inside five simple words.
Issue #52 | How AI agents work under the hood - the execution loop that enables reasoning, tool calling, and iterative problem…
Part 5 let the model pick one tool. Real agents chain tools — search, then calculate, then answer. This post builds the ReAct…
Un agente de IA es un LLM metido en un bucle que razona, elige herramientas y ejecuta acciones hasta cumplir una meta. Te…
