An AI agent combines a model with tools and a process for choosing actions. It might search a document library, read an order status and draft a response. A more capable workflow might also create a support ticket or request a refund. The business risk changes as soon as the system can alter records rather than only write text.
Calling software an agent does not establish that it is autonomous, reliable or suitable for sensitive decisions. Start by listing exactly what the proposed system can read, write and approve. Those permissions explain its practical scope more clearly than a diagram of models.
Break the workflow into actions
Consider a hypothetical customer asking why a delivery is late. A useful sequence is to identify the customer, retrieve the authorised order, check the delivery record, explain the latest status and offer an appropriate next step. Each action has a different source of truth.
The language model can interpret the question and propose an action. The order service must still decide whether the requester can access that order. The ticket service should validate required fields. A refund endpoint should enforce policy and approval limits independently of what the model says.
Identify three boundaries
Read boundary: which records and fields can enter the model context? A support role may need delivery status but not a full financial history.
Action boundary: which changes can the workflow submit? Drafting a note and cancelling an order should not share the same permission.
Approval boundary: which actions need a person, and what does that person review? Showing a button labelled approve is insufficient if it hides the actual amount or customer record.
These boundaries should be enforced in application services. A prompt asking the model to behave carefully is useful instruction, but it is not an authorisation mechanism.
Plan for uncertain inputs
Customers use ambiguous language, reference the wrong order or paste instructions from another website. Retrieved documents can also contain hostile instructions. Treat those materials as data, and prevent them from expanding the agent's privileges.
A safe failure can be a clarification question, a draft awaiting review or a handoff to staff. It should preserve enough context for the staff member to continue without guessing why the workflow stopped. Avoid repeated retries that keep performing the same external action.
Make outcomes inspectable
An activity record should show the request, selected tool, validated parameters, result and any human approval. Keep sensitive information out of general logs. A reviewer needs to distinguish a model-generated explanation from the actual tool result.
For example, a successful ticket creation has a ticket identifier returned by the ticket system. The agent should not infer success from a plausible sentence. If the system timed out after accepting the request, an idempotency or status check is needed before retrying.
Define a first release
Choose a bounded workflow with clear evidence and a low-impact action. Order-status lookup plus ticket drafting is easier to evaluate than an agent allowed to negotiate refunds, change addresses and place purchases in one conversation.
Create test cases for wrong identities, unavailable tools, duplicated requests and unclear intent alongside normal requests. Measure task completion and inappropriate actions separately. A system that answers fluently but writes to the wrong record has failed its core business requirement.
Primary references
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