BonrockWhat an AI Agent Does Between Your Request and the Result
An agent can choose and carry out several steps toward a task. Its usefulness depends on the tools, state, permissions and stopping rules around that loop.
Ask an assistant why a test is failing and it may explain the error. Give it access to the repository and a task to fix the failure, and it can read the files, make a change, run the test and use the result to decide what to do next. That repeated choice of action is a useful way to understand an AI agent.
The interface can be a chat window in either case. What matters is which steps the system carries out and how much control it has over them.
Follow one run
A coding agent starts with the user's task and the context it can read. It chooses a tool, receives the result and decides whether to continue. A failed test may lead it back to the code; a passing test may lead it to inspect the diff and report what changed.
The loop needs a stopping rule. “The selected test passes” is checkable, but it may be insufficient if the fix breaks another feature. A better task names the expected behavior, the required checks and the actions the agent is allowed to take. Committing or deploying can require separate authorization.
An agent can operate for a short bounded task. It does not have to run all night, and asking a person to approve a sensitive step does not stop it from being an agent.
Tools and state make the loop useful
Tools give the system a way to observe or change something outside its generated answer. Reading an order, searching documentation and updating a record are different capabilities. Expose the narrow operations the task needs.
State lets the system track what it has tried and what it found. A short task may fit in conversation history. A longer one may need saved notes, a job record or a database. Store completed actions explicitly so a restart does not resend a message or repeat a payment.
A model can choose an action from the information available to it. It cannot recover a missing business rule merely because the surrounding software is called an agent. If the task needs an approval policy, provide one and enforce the relevant limits outside the model.
Fixed workflows still have a place
Consider invoice processing: receive the document, extract fields, match a purchase order and route it for review. Much of that can follow ordinary code and written rules. A model may help interpret an unusual document or propose how to resolve a mismatch.
Let the predictable steps remain predictable. An agent is useful where the next step depends on a result that is difficult to cover with fixed rules. Giving a model control over the whole process adds decisions to test and monitor.
There is no universal boundary between workflow and agent. For a design review, describe the actual choices the model makes rather than arguing over the label.
Check the action as well as the answer
An incorrect answer can mislead a reader. An incorrect tool call can also change records, disclose data or incur a charge. Test the actions with the same care as the generated text.
A support assistant might retrieve an order and draft a reply, while a refund beyond policy waits for an authorized person. The review should show the source record, the proposed action and its effect. A generic “approve” button gives the reviewer too little information.
Set limits on elapsed time, tool calls and spending. Record failures and interrupted runs. If the agent cannot establish that a write succeeded, it should reconcile the state before trying again.
Start with a task whose result someone can check. Compare the complete run, including corrections and review, with doing the work directly. That tells you whether the agent is useful in this setting and where to give it more or less responsibility.
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