{"slug": "ai-agents-explained-the-thought-action-observation-loop", "title": "AI Agents Explained: the Thought-Action-Observation Loop", "summary": "An engineer demonstrates how AI agents use a Thought-Action-Observation loop to solve multi-step tasks by calling tools like calculators and search. The agent iterates until completion, with each real observation fed back into context to guide subsequent decisions. The implementation includes safety measures such as iteration caps and input validation.", "body_md": "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.\n\n🤖 **Run the agent:** [https://dev48v.infy.uk/ai/days/day11-agents.html](https://dev48v.infy.uk/ai/days/day11-agents.html)\n\nYou 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.\n\n``` js\nwhile (true) {\n  const step = await llm(history);            // model emits a Thought + Action\n  if (step.type === \"answer\") return step.text;\n  const result = tools[step.tool](step.args); // run the tool\n  history += `Observation: ${result}`;         // feed the real result back\n}\n```\n\nThe model writes a **Thought** (plan), emits an **Action** (tool + args), your code runs it and returns an **Observation**, which goes back into context. Then it thinks again.\n\nEach observation is REAL, fed back before the next decision — so it's not guessing the tip amount, it sees `126`\n\nfrom the calculator. And it plans the steps itself: \"population of France's capital, doubled\" becomes search → then calculator, chained because the model worked out the dependency.\n\nCap iterations (no infinite loops), validate tool inputs, gate risky actions (email, payments) behind approval. Autonomy is the point; limits make it safe.\n\n[Run a task](https://dev48v.infy.uk/ai/days/day11-agents.html) and watch the Thought→Action→Observation trace build.", "url": "https://wpnews.pro/news/ai-agents-explained-the-thought-action-observation-loop", "canonical_source": "https://dev.to/dev48v/ai-agents-explained-the-thought-action-observation-loop-3mgb", "published_at": "2026-06-20 07:07:51+00:00", "updated_at": "2026-06-20 07:36:57.457599+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "large-language-models", "developer-tools"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/ai-agents-explained-the-thought-action-observation-loop", "markdown": "https://wpnews.pro/news/ai-agents-explained-the-thought-action-observation-loop.md", "text": "https://wpnews.pro/news/ai-agents-explained-the-thought-action-observation-loop.txt", "jsonld": "https://wpnews.pro/news/ai-agents-explained-the-thought-action-observation-loop.jsonld"}}