An AI agent does more than answer once. It works toward a goal through a loop: inspect the current state, choose an action, use a tool, read the result and decide what to do next.
That extra freedom can help with messy tasks, but it also creates more ways to fail.
Chatbot, workflow or agent
A chatbot waits for a message and returns a response. A workflow follows a path defined in advance: collect inputs, draft, review, export. An agent chooses at least part of its path while it runs.
Suppose you need a weekly competitor brief. A workflow can tell a person which sites to check and how to compare the findings. An automated workflow can fetch the same approved feeds on a schedule. An agent might decide which source to open next, revise its search after a weak result and stop when it believes the brief has enough evidence.
The agent is more flexible. It is also harder to predict and test.
What sits inside an agent
Most agent systems combine a model, instructions, tools, state and a stopping rule. Tools may read files, search approved data, write a draft or make a change in another system. State carries useful information between steps. The stopping rule says when the goal is complete or when a person must decide.
The tool permissions matter as much as the model. Reading a product catalog is different from changing a price. Drafting an email is different from sending it.
Use the least autonomy the job needs
Anthropic’s engineering guidance recommends starting with simple prompts and adding agent behavior only when simpler systems fall short. That is a sensible operating rule. If a fixed five-step workflow finishes the job, an agent adds cost and uncertainty without giving you much back.
Agents become more useful when the path cannot be known in advance, the system must react to intermediate results and the possible actions can be constrained. Research across a changing source set may fit. Issuing a refund without approval does not.
Define permissions before prompts
Write an action table before connecting tools. For each action, choose read, draft, ask for approval or block. Keep financial changes, publishing, external messages, deletion and access changes behind explicit human approval.
Also define the evidence the agent must retain. A research agent should return source URLs and the text that supports each claim. A file agent should produce a change list. An operations agent needs a log that lets someone reconstruct what happened.
Test the loop with awkward cases
Happy-path demonstrations tell you little. Test missing data, conflicting instructions, tool failure, an irrelevant search result and a request that falls outside policy. Confirm that the agent stops instead of filling gaps with guesses.
An agent is ready for broader use only when you can answer four questions: what can it see, what can it change, when does it ask and how do you undo its work?