An agent can make sense when a task needs interpretation and a variable sequence of actions, and you can bound its access, evaluate its behavior and recover from mistakes. Use a fixed workflow when the path is already known. Use assistance when a person should make the final decision.
Define what the system is allowed to do.
The useful distinction is not the label on the product. It is whether the system follows a fixed path, suggests an action to a person or chooses actions within a defined boundary. Write down the permitted inputs, tools, changes and stopping conditions.
Consider a document intake process. Extracting a few fields can be a bounded interpretation step inside a conventional workflow. An assistant might prepare a response for review. A system that chooses sources, requests missing information and updates business records has a broader responsibility and needs stronger controls.
Ask whether the path really needs to vary.
If you can specify the necessary steps in advance, an ordinary workflow is often easier to test and operate. AI may still help with one interpretation step without controlling the sequence. More autonomy should solve an actual limitation, not make a demonstration look sophisticated.
Variable tasks can justify an agent when choosing the next action is useful and the available actions are safe enough to expose. That is a narrower claim than saying a model can perform the whole role of a person.
Permissions belong outside the model.
A model's instructions are not a substitute for application access controls. Restrict the information and actions available through the surrounding system. Validate outputs and require approval before consequential changes where appropriate.
Information retrieved from documents or external sources may contain misleading instructions. Treat it as task data, not authority to expand access or change the operating rules. Design tool interfaces so an unexpected request can be rejected without trusting the model to police itself.
Evaluate the hard cases, not just the successful demo.
Build a representative set of tasks with expected outcomes. Include missing information, ambiguous instructions, unavailable tools and attempts to exceed the intended boundary. Define what counts as a safe failure, not only a correct answer.
Watch the full task: quality, human review, time, model cost and recovery. A result that requires substantial checking can still be useful, but it should be compared honestly with a simpler alternative. Repeat evaluations when models, prompts or tool behavior change.
Keep a person and a recovery path in the system.
Someone needs to own the result, inspect what happened and decide when to stop or reverse a run. Use logs appropriate to the task without retaining unnecessary sensitive content. Set limits on actions and cost; surface unresolved work rather than presenting it as completed.
Do not begin with unrestricted access, irreversible actions or a task whose success nobody can define. A reviewed assistant or a deterministic workflow may be the better system. AI earns its place by improving capability under real constraints.
Further reading: OWASP’s guidance on prompt injection explains indirect instructions, least privilege and review for high-risk actions.
A quick decision guide.
| Your situation | A useful direction |
|---|---|
| Known sequence, exact rules | Use a workflow. |
| Interpretation within one step | Evaluate a bounded AI capability. |
| Variable actions, safe boundaries | Test a constrained agent. |
| Consequential decision | Keep accountable human review. |