An assistant that writes a draft and an agent that changes business software do not have the same impact. In the second case, the AI receives tools and can trigger actions. This can remove repetitive manual work, provided the system’s permissions are defined precisely.
Start with a limited action
Consider a fictional example: preparing a follow-up record after a meeting. The system can extract decisions, suggest tasks and show a preview. A person then checks the result before creating it in the project management tool. This makes it possible to assess the quality of the preparation while retaining a clear approval step.
Going straight to sending messages, changing several records or executing a chain of invisible actions makes mistakes harder to understand. A first automation benefits from an explicit input, a bounded action and a result that can be checked.
Provide only the tools the task needs
Making a connector available should not grant access to every function of a service. Reading a document, creating a draft and deleting a folder are different permissions. Access must match the user’s role and the agent’s scope.
Credentials stay on the server. The model may propose parameters, but the server must validate them: permitted destination, content type, size, resource ownership and the right to perform the operation. A text instruction asking the model to be careful is not an access control.
Anticipate misleading content
OWASP describes indirect injection as malicious instructions placed in content consulted by the model, such as a page or message. With an agent, the consequences can extend beyond an incorrect answer if that content influences an action.
Treat tool results and documents as data to examine. Limit the available actions independently of the text received. For sensitive operations, show the user the destination, the data involved and the expected effect before asking for approval.
Handle errors without multiplying actions
An external service may fail to respond even though the action was completed. An automatic retry can then create a duplicate. Distinguish a confirmed failure from an uncertain outcome and, where the service supports it, use a stable identifier for the same operation.
Monitoring should also bound attempts, elapsed time and spending. If the agent loops or a connector becomes unavailable, the user needs to see the task’s state and understand what requires intervention. A useful history records operations and outcomes without retaining unnecessary sensitive information.
Evaluate the whole process
The NIST profile for generative AI encourages organisations to examine risks throughout the lifecycle. For a business agent, our practical checklist covers output quality, rejected actions, service failures, access revocation and recovery.
Measure the time spent reviewing the work as well. Automation is useful only if the complete workflow becomes simpler or more reliable for the selected task. The studio’s solutions and our project scoping guide help prepare that discussion around a real use case before adding connectors.
