An AI agent is a system that doesn’t just answer one question and stop — it takes a goal, chooses actions (often by calling tools or APIs), observes what happened, and loops until the goal is met or it gives up. That’s a different beast from a chatbot that runs one prompt and returns one reply. Agents are why you can say “summarize my inbox and draft three replies” and the system actually opens the inbox, reads, calls the LLM, writes drafts, and shows them to you.
The core loop is simple: state (goal, context) → decide (what to do next) → act (run a tool or ask the LLM) → observe (get tool output or model output) → update state → repeat. The hard part is making “decide” and “act” robust: the model has to pick the right tool, pass the right arguments, handle errors, and not loop forever. That’s where frameworks and patterns like ReAct, tool-calling APIs, and guardrails come in.
Agents can be single-LLM (one model that reasons and picks tools) or multi-agent (several specialized models or roles that hand off to each other). They can have access to search, code execution, file systems, or custom APIs. The unifying idea is autonomy within a defined scope: you set the goal and the rules, the agent does the steps.
Failure modes are real: the model might call the wrong tool, misparse output, or spin on a task without making progress. Production agents need timeouts, step limits, and human-in-the-loop checkpoints for anything irreversible. The best agents are also observable — you can see the reasoning and tool calls so you can debug and improve them.
Agents are where LLMs move from “helpful assistant” to “autonomous worker.” Expect more tooling, better tool schemas, and clearer patterns for when to use agents versus a single call.
nJoy 😉