Agentic AI: When a Model Can Take Action
An ordinary model call takes input and returns output. An agent gets a goal, access to a set of tools and enough control to decide what to do next.
That control is useful for tasks where the next step depends on the previous result. It is also where the risk starts. A model that can call an API can make a wrong decision with real consequences, so the application still needs limits, validation and approval points.
The agent loop
The loop has three parts:
- Decide: use the goal and current context to choose the next action.
- Act: call a tool such as search, a database query or an internal API.
- Check: inspect the result, update the plan or stop.
The loop may run once or several times. The number of iterations is an implementation detail; the important part is that the agent can use feedback instead of guessing the whole path in advance.
When an agent is the right tool
I use an agent when the task has several valid paths and the system needs to choose between them. Research, support triage and code maintenance are reasonable examples.
I use a normal workflow when the steps are known. If the process is “validate the form, charge the card and send a receipt”, explicit application code is easier to test and safer to operate.
The boundaries matter
Give an agent narrow tools with typed inputs. Validate tool arguments on the server, keep credentials scoped, cap retries and log the important decisions. For actions such as refunds, publishing or sending messages, make human approval part of the design.
The useful question is not whether a system is autonomous. It is whether giving the model control over this particular decision improves the outcome enough to justify the extra uncertainty.
For a working example, see Building AI Agents with the Vercel AI SDK.