Add an AI agent by choosing a specific user task, exposing authorized tools, and evaluating the complete workflow. Start with assistance or draft actions, then expand autonomy only where permissions, recovery, and observed reliability support it. Done this way, agents become part of the product rather than a chat box bolted onto it.
Choose a job with a clear finish
Customers now compare existing software with AI-native alternatives, but a general chat box is not a product strategy. Choose a task such as investigating an invoice discrepancy, preparing a shipment quote, or finding the correct procedure for a support issue. Define what successful completion looks like and how a user can verify it.
The best initial workflow has useful existing data and a manageable action surface. If the underlying process is ambiguous or its data is unreliable, adding a model will amplify those problems. Fix the task definition before expanding the agent. In older products, this sometimes means modernizing the workflow or extracting a service first.
Design tools as product interfaces
A tool should represent a deliberate capability, with an input schema, authorization, validation, and a predictable result. Prefer a read-only order lookup or a draft-quote operation over unrestricted database access. The backend should enforce business rules even if the model supplies an unexpected argument.
Separate observation from action. Looking up a customer record, drafting a message, and sending it have different consequences. The interface should make that difference visible and require review where the business process calls for it.
Evaluate the whole journey
Create cases for ordinary requests, ambiguous instructions, missing data, permission failures, tool timeouts, and malicious text inside retrieved material. Measure whether the user reaches the intended result, not just whether the response sounds good. Keep some cases independent of implementation work.
Record cost and latency as well as correctness. An agent that produces a useful answer after an unacceptable delay may need a simpler workflow, caching, fewer tool calls, or a different model. Models change quickly, so treat these as engineering decisions with evaluations that can be rerun, rather than prompt-only problems.
Release with ownership and a fallback
Tell users what the agent can do and where its answer came from. Provide a way to correct or abandon the workflow. Maintain action logs appropriate to the application and define who investigates failures. A model or tool change should trigger relevant evaluations before release.
Our product AI agents service combines product integration with the evaluation and operating work around it. A first release can be a focused assistant that prepares evidence or a draft action; its results then guide whether further automation is worth the added complexity. If you are unsure where agents fit in your product, get a free application review.
Common questions
Does every agent need to take actions?
No. A read-only investigator or drafting assistant can create value with a smaller operational risk and simpler release process.
Can a prompt enforce tenant security?
No. Tenant and user authorization must be enforced by the application and tool layer, independently of model instructions.
Further reading
Primary references for the concepts discussed. Recommendations and examples are InfuseAI’s editorial guidance.