What are AI agents and how are they different from chatbots?

The difference is action and autonomy. A chatbot responds to you with text; an agent pursues a goal by taking actions in the world, deciding its own next steps, and continuing until it's done or stuck. Concretely: a chatbot can tell you how to check your server logs. An agent can be given 'find out why the deploy failed', then run the commands, read the output, form a hypothesis, check the config file, and report back with a diagnosis — several steps it chose itself based on what it found. The pieces that turn a model into an agent: 1. **Tools.** The ability to call functions — search the web, run code, read and write files, query a database, call an API. Without tools it can only produce text. 2. **A loop.** Rather than one response, it iterates: decide the next action, execute it, observe the result, decide again. This is the core structural difference. 3. **Memory or state.** Carrying context across steps so step nine knows what happened at step two. 4. **A goal and stopping condition.** Something to work toward and a way to know it's finished. Where they genuinely work well right now: tasks with clear success criteria and fast feedback. Coding agents are the standout case, because code either compiles and passes tests or doesn't — the environment provides the correction signal automatically. Research tasks with verifiable outputs, data processing pipelines, and multi-step operations on well-defined APIs also work. Where they struggle, honestly: - **Error compounding.** If each step is 95% reliable, twenty steps is about 36% reliable. Long chains fail in ways that are hard to predict, and this arithmetic is the central engineering problem. - **Getting stuck in loops**, repeating a failing approach, or confidently going off-track without noticing. - **Tasks without a clear success signal**, where nothing tells the agent it's wrong. - **Anything with real-world consequences** — sending emails, spending money, deleting things. Autonomy plus irreversibility is a bad combination, which is why serious systems keep a human approval step on consequential actions. How to think about the current state: agents are genuinely useful in narrow, verifiable, bounded domains, and are oversold as general autonomous workers. The gap between an impressive demo and a system that works reliably on the tenth run with messy real inputs is very large, and closing it is mostly unglamorous engineering — evaluation, guardrails, retries, and knowing when to hand back to a human. If you're building: keep the loop short, verify each step where you can, log everything, and put a human in front of anything you can't undo.