What is an AI Agent?
A chatbot answers your question. An agent does the thing. The difference is tools, autonomy, and a loop: an agent can search the web, run code, send emails, and keep working until the job is done — not just until the next response.
A chatbot is one-and-done. An agent keeps working — planning, using tools, checking results — until the goal is reached or it determines it can't be.
Three things separate an agent from a chatbot.
Tools
A chatbot can only talk. An agent can do: search the web, read files, run code, send emails, query databases, call APIs. Tools are what turn language into action.
Autonomy
A chatbot waits for you. An agent decides its own next step. Given a goal, it plans, picks tools, executes, checks results, and adjusts — without asking permission for every action.
The Loop
Act, observe, decide, repeat. The agent runs in a cycle: do something, check what happened, decide what to do next. This loop continues until the task is done or the agent hits a limit.
Agents turn language into real-world action.
The simplest agent is a coding assistant: you say "fix the authentication bug" and it searches your codebase, identifies the issue, edits files, runs tests, and commits the fix — all without you touching a keyboard. More advanced agents manage multi-step workflows across different systems: "research competitor pricing, update our spreadsheet, and draft a summary email to the team." Each step uses different tools.
Agents can also coordinate with other agents. One agent researches, another writes, a third reviews. They pass work between each other like a team — each specialized, each with its own tools and context.
Every agent runs the same basic loop: act, observe, decide.
Plan
Given the goal, the agent breaks it into steps. "To fix the auth bug, I need to: find the auth code, identify the issue, write a fix, test it, commit it."
Act
Execute the first step. Search the codebase. Call an API. Run a command. The agent picks the right tool and uses it with the right parameters.
Observe
Check what happened. Did the search find the right file? Did the API return an error? Did the test pass? The agent reads the output and decides what to do next.
Decide & Repeat
If the step worked, move to the next one. If it failed, try a different approach. If stuck, ask the human. This loop continues until the goal is reached or the agent exhausts its options.
Agents are powerful — and they fail in predictable ways.
Getting Stuck in Loops
The agent tries something, it fails, it tries the same thing again. Without a limit on retries or a way to detect repetition, it can spin forever. Every agent framework includes loop detection for this reason.
Taking Wrong Actions
The agent misunderstands the goal and does something destructive — deletes the wrong file, sends an email to the wrong person, runs a command that breaks production. This is why agents need guardrails and confirmation gates.
Losing Context
Long agent runs fill up the context window. Old information gets dropped. The agent forgets what it already tried or what it learned earlier. Memory systems and summarization help, but they're not perfect.
Cost Spiral
Every tool call and every loop iteration costs tokens. An agent stuck in a retry loop or a long debugging session can run up significant bills. Production agents need cost limits and monitoring.
The easiest way to see an agent in action is a coding agent.
Claude Code and OpenCode are terminal-based coding agents. Install one, point it at a project, and describe a task in plain English. Watch it search files, propose edits, run tests, and commit changes — all autonomously. It's the fastest way to understand what agents can do because the feedback loop is immediate: you see the code change, the tests run, the commit appear.
For non-coding workflows, OpenClaw and Hermes Agent bring agent capabilities to messaging apps — send a message on Telegram and an agent researches, schedules, or automates in the background. The agent paradigm is the same regardless of the surface: goal, tools, loop.
"A chatbot tells you what it knows. An agent goes and finds out."
Agents are chatbots plus tools plus a loop. The loop is what makes them autonomous — they keep working until the job is done, not just until the next response.
Start with a coding agent to learn the pattern. The feedback loop is immediate, the stakes are low (git can undo anything), and the capabilities are concrete.