Before choosing a model or framework, define the business workflow the agent must improve. An AI agent is useful when it can interpret a request, retrieve the right context, use approved tools, and complete or route work with a clear outcome. A vague goal such as “make the business more intelligent” is not a build plan.

Start with one measurable workflow

Choose a repetitive process with a visible starting point and finish line. Examples include answering questions from approved company material, qualifying an enquiry, preparing a support response, or routing a request to the correct team. Record the current process, systems involved, exceptions, and where a person must remain accountable.

Define the agent boundary

• What may it read?
• Which tools may it call?
• Which actions require confirmation?
• When must it hand off?
• What does successful completion mean?

Keep the first version narrow. A reliable agent that completes one workflow is more valuable than a broad assistant that makes uncertain changes across many systems.

Plan evaluation before launch

Create a test set from real questions and edge cases. Measure answer quality, source selection, tool-call accuracy, completion rate, escalation quality, latency, and cost. Include missing information, conflicting instructions, prompt injection, unavailable systems, and requests outside the agent’s authority.

Read RAG versus traditional chatbots, workflow orchestration, and scaling agentic workflows.