AI Agents for Business Workflows: Evaluating Permissions and Reliability

A Dubai distributor wants an AI agent to turn approved purchasing requests into draft orders. The software can read the request, look up a supplier and enter line items in the purchasing system. During the demo it completes the job in a minute. During testing it receives a request with two similar supplier names, retries after the purchasing system times out and produces two draft orders. The company now has to ask which parts of the workflow the agent was authorized to run and how a supervisor could tell what actually happened.

An agent can use software tools to take a series of steps toward a goal. That makes it useful for work that crosses systems. It also means the business has to test what the agent can change, under whose authority and how errors can be contained. Before giving it write access, decide whether the workflow needs an agent at all. A fixed rule can be easier to verify when the inputs and decisions are already structured.

Define the operations before granting access

Begin with a list of actions the service needs for the approved job. In the distributor example, an employee may authorize the agent to read assigned requests and create drafts in one UAE entity. A separate buyer may approve the final order. Changes to supplier bank details or payment instructions belong in their own process. Give each operation its own authorization rule and enforce it in the service that performs the action.

Set an execution budget alongside the permissions. For example, limit the number of orders a run can create, the entities it may touch and the volume of work it can queue before a supervisor reviews it. An individual draft may meet every validation rule while a repeated sequence creates a backlog of hundreds. Test a batch above the expected limit and confirm that staff can distinguish the items already processed from the ones put on hold.

Check which identity the agent uses when it calls another system. A shared account with broad access may see records the employee initiating the job cannot open. An agent used across entities can also carry information from one session into another if its work history is shared incorrectly. Test identities with ordinary employee permissions and inspect what actually reaches the receiving service. A user interface that hides a button provides weak protection if the underlying credential can still perform the operation.

OWASP's guidance on excessive agency describes the risk of giving a model too much functionality, permission or autonomy. Its guidance includes limiting what tools can do and requiring approval for consequential actions. Apply those constraints to the execution path. A sentence telling the model to refrain from releasing orders should be backed by a check that refuses release without a valid approval. OWASP on excessive agency

Make approval specific to what will happen

An approval screen should show the action, entity, supplier, amount, currency and relevant line items. The person approving it should know whether the action creates a draft, commits the business or sends a message outside the company. Give the approver access to the original request and make any disputed fields easy to spot. The employee needs enough information to detect a mistake without repeating the entire workflow.

Bind approval to the material facts the employee reviewed. If the supplier or price changes after approval, return the proposed action for another decision. Test this deliberately by updating a draft while it waits in the approval queue. Test a second case where the approver's access is withdrawn before the action executes. Verify that the application handles the change according to the rules of the receiving system.

ADNOC’s public post about a framework for local manufacture and supply offers context for the commercial commitments a procurement workflow may support. In the distributor example, the authority to prepare a draft and the authority to commit to a supplier agreement sit at different stages.

Account for pressure on the person approving the work. A queue filled with identical approval cards encourages quick decisions. Sample approved items and measure review time alongside detection of errors. If reviewers regularly open the original record to resolve a missing field, fix the approval view or move that category back to staff. A human checkpoint works when the person can understand the proposed action and has the time and authority to challenge it.

Decide what to do when the outcome is uncertain

A timeout does not tell you whether a remote operation happened. The purchasing system might have saved the draft and then lost the response. Sending a fresh request can create a second draft. AWS's Builders' Library describes how an API can use a caller-provided identifier to recognize a repeated request and make retries safer. For a purchasing workflow, the receiving system needs to honor that identifier or provide another way to confirm the result before the agent tries again. AWS on making retries safe with idempotent APIs

Use a stable reference for the intended operation. Keep that reference if a job is retried because the network dropped. If an employee changes the order and starts a new action, handle that as a new intent under the normal approval rules. Exercise both paths in testing. Replay a request after a timeout and check that only one record exists. Then submit a legitimate second order and verify that duplicate protection does not silently drop it.

A multi-step job can leave a partial result. The agent may create a draft but fail to attach the requested documents or notify the buyer. Decide whether the draft should stay open, be cancelled or move to a named queue. Record the external reference at each step and show the operator which actions have been confirmed. If undoing a step requires a separate action, give that action its own checks. Cancelling a draft and reversing a supplier communication have different consequences.

Put a limit on retries and give uncertain cases to a person who can inspect the destination system. Test a restart in the middle of a job and test two workers picking up the same request at once. Verify which worker can advance the job and how a supervisor recovers an abandoned item. These tests are especially useful when an agent runs on a schedule and the next cycle might begin before the previous one has finished.

Test the workflow against business records

Build an acceptance set from ordinary jobs and controlled failures. Include a simple approved request, missing or contradictory instructions, two suppliers with similar names, an expired approval and a document that tries to instruct the agent to use a different supplier. Simulate a slow purchasing system and a destination that rejects a valid-looking request. Include a cross-entity access test and a case with Arabic text and an English item code if those appear in the actual queue.

Judge the final state using the destination system. For a routine case, check that the intended draft exists with the right items, currency, supplier and entity. For an unclear case, check that the tool asks for the missing detail or routes it appropriately. For an unauthorized attempt, check that no protected data was returned and no change occurred. The agent's message can help explain the result. The purchasing record and its event history show whether the action happened.

Consider an illustrative trial of 120 jobs. The agent creates 90 correct drafts that staff can accept without correction, routes 20 legitimate exceptions to a buyer and produces 10 unintended outcomes. A report could say that 110 jobs followed their intended path, or about 92%. The decision still depends on the ten failures. If one was a cross-entity write, fix the permission boundary and retest it before opening the workflow to more staff. If several were recoverable input errors, measure the time buyers needed to correct them and compare it with manual entry.

Record the configuration used for the acceptance set. A change to the purchasing connector can affect duplicate handling even when the model is unchanged. Keep the retry, approval and access cases available for the next release.

Track operating cost and effort across successful and unsuccessful jobs. Count the time spent approving drafts, checking uncertain outcomes and responding to queue failures. If the agent saves four minutes on 90 correct drafts, it frees six hours in the trial before accounting for new review or recovery work. If investigating the 10 failures takes 30 minutes each, five of those hours are already absorbed. Use comparable manual cases to establish which work is genuinely new and which a buyer would have had to do anyway.

Give the supervisor a way to see and stop the work

Keep a record of who initiated each job, the operation requested, the authorization decision, any approval and the destination reference. Log when the system rejected a request and why. Decide which people can open those records and what sensitive fields need to be excluded. The objective is to reconstruct a decision without copying supplier contracts or credentials into a troubleshooting feed.

The UAE Government's summary of the National Cyber Security Policy for Artificial Intelligence describes governance and secure operation among its themes. In a rollout review, turn those themes into evidence about the proposed workflow. Ask who owns access, who can pause actions, how incidents are reviewed and what the team checks before resuming. Verify the relevant requirements for the entity with the people responsible for the deployment. UAE National Cyber Security Policy for Artificial Intelligence

Rehearse the pause with the people who will use it. They should be able to stop new writes, see jobs already in flight and move urgent requests to staff. Have the team confirm which orders reached the destination before allowing any replay. Restore access in stages and verify a small batch before clearing a backlog. An emergency switch is useful when people know how to complete the work it interrupts.

Approve the smallest operating scope that the evidence supports. The distributor might allow read access and draft creation for one entity while keeping every release under a buyer's control. A later proposal to send orders to suppliers needs its own permission, approval and recovery tests. The decision record should identify which operations were exercised, how failures were handled and who will monitor them when the agent leaves the trial environment.