llm agent
An AI agent is a system built around a large language model that does not just answer a question but pursues a goal by taking actions in a loop. Where a plain chatbot maps one prompt to one reply, an agent runs a cycle: it reasons about what to do next, calls a tool to actually do it, observes the result, and repeats — continuing until the task is finished. This loop, plus the tools the model can reach, is what turns a fluent text predictor into something that can search the web, run code, query a database, or operate other software on a user's behalf, and it's the specific mechanism — not a bigger or smarter model — that separates an agent from a chatbot.
The core mechanism is an observe-reason-act loop. The agent is given a goal, the model reasons about the next step, it emits an action (a tool call), the environment runs that action and returns a result, and the result is fed back into the model's context for the next turn. This interleaving of reasoning and acting — popularized as ReAct by Yao and colleagues in 2022 — is what lets the model course-correct: it can react to what a tool actually returned instead of committing to a plan blindly. The loop ends when the model decides the goal is met and emits a final answer.
Memory and planning separate a toy from a workhorse. Short-term memory is the context window itself — a scratchpad of the conversation and recent observations — while long-term memory offloads facts to an external store, often a vector database, that the agent retrieves from as needed. Planning adds structure on top of the raw loop: decomposing a big goal into subtasks, reflecting on failures, and retrying. More capable agents plan, criticize their own work, and sometimes delegate subtasks to specialized sub-agents in a multi-agent setup.
Autonomy is a spectrum, and more is not always better. At one end is a single tool call inside an otherwise normal chat; in the middle is a fixed multi-step workflow; at the far end is a self-directed agent that decides its own steps until done. Greater autonomy unlocks harder tasks but sacrifices predictability and control, which is why side-effecting actions — sending email, spending money, changing files — are usually gated behind confirmation or guardrails.
The hard problems are reliability, cost, and safety, not raw capability. Errors compound over long horizons — a wrong step early can derail everything after it — and every turn is another LLM call, so agents are slower and more expensive than a single response. Tools fail, environments change, and evaluating open-ended agent behavior is genuinely hard. Much of real-world agent engineering is about constraining the loop: good tools, retries, verification steps, human approval for risky actions, and tight scoping of what the agent is allowed to do.
Read agents through an action-loop lens rather than a smarter-chatbot lens: the leap is not that the model knows more, but that it is placed inside a loop where it can decide what to do next, do it with a real tool, and react to the outcome. Capability then comes as much from the tools, memory, and control structure around the model as from the model itself — which is why building a good agent is mostly about engineering a reliable loop, not just prompting a smarter one.