Use fixed rules when they fit
If the requirement is “email the account owner when an invoice becomes overdue,” a conventional rule may be sufficient. AI becomes more relevant when inputs are varied and require interpretation, such as understanding a free-text enquiry. Do not add a model to a deterministic calculation simply because AI is available.
Separate interpretation from action
A system can classify a request with AI and then apply ordinary business rules. For example, the model suggests a service category while the application decides which team is permitted to receive it. This separation makes failures easier to inspect and prevents generated text from becoming unrestricted authority over your systems.
Understand the added responsibility of agents
An agent can be designed to choose tools or steps toward a goal. That flexibility introduces questions about permissions, stopping conditions and unexpected actions. Specify which tools it may use, what information it may access and which operations require approval. Tool access should be narrow enough to match the business task.
Design a purchasing example
An assistant might gather supplier information and prepare a comparison. Sending a purchase order is a different action with financial consequences. Keep the comparison stage separate from approval and dispatch. A recommendation should not silently cross the boundary into a commitment just because the software can call another API.
Choose the simplest adequate design
Draw the workflow and mark each step as a fixed rule, an interpretation task or an approval. Start with rules and bounded AI assistance where possible. Introduce more autonomous behaviour only when you can explain the benefit, test the failure paths and monitor the resulting actions. Complexity needs a business reason.
Published by DevMania with AI assistance. Examples illustrate planning approaches and do not claim client results. Check current provider documentation before implementing product-specific features.