Building AI Agent Capabilities Into Your Product

An AI agent can be part of what your company offers customers, not just a way to do internal work faster. The starting question is: what could a user do with your product that they cannot do today?

A useful answer describes an experience, not a technology. A teacher might prepare material using curriculum context. A customer might explore a complex catalog through questions. A professional might work with information spread across several sources.

Whether any of these needs an agent depends on the task.

Start with a user and a task

Choose one person, one situation, and one useful result. “Add AI” is not a product requirement. “Help a teacher prepare a lesson draft from a learning objective and review it before saving” gives a team something to design and test.

Describe what the user provides, what the product returns, and what the user should be able to inspect or change. Decide what should happen when the request cannot be completed.

Decide whether the capability needs an agent

Not every AI feature needs an agent. A single model call may be enough to generate a draft. Retrieval can supply relevant source material. An agent becomes useful when the system needs to choose steps or tools based on what it encounters.

That flexibility adds engineering work. Your team must define which tools the system may use, what information it may access, and when it must stop or ask the user.

The goal is a useful product experience, not the most elaborate agent system.

Connect knowledge and actions to the experience

A product may need answers grounded in its own content rather than a model’s general knowledge. Start with the sources: are they current, can the user access them, and can an answer point back to the supporting material?

If the feature can take actions, distinguish a suggestion from a change to real data. Showing a proposed edit and saving that edit are different permissions. Make those boundaries visible to the user and enforce them in the system.

Build a first version you can evaluate

Keep the first version focused enough to inspect. Agree on representative requests, incomplete inputs, and cases the feature should decline. Use authorized or anonymized examples.

Evaluate the experience as well as the output:

• Can the user complete the intended task?
• Is the answer or generated material suitable for that task?
• Can the user inspect sources and correct the result?
• Does the feature handle missing information without inventing it?
• Are response time and usage cost acceptable for the intended experience?
• Does the system stay within its permissions?

Define acceptance criteria for your product. There is no universal score that makes every agent ready to release.

An example: educational product capabilities

In Ketzai Agent, the modular agent system my team is building at Ketzai, shared coordination supports different educational tasks. Teacher Lab helps teachers prepare and review educational planning materials. Ketzai has a presence in Panama and the Dominican Republic, with expansion to Colombia planned.

I lead the team’s work on orchestration, workflow routing, the core runtime, and tools for testing and evaluation. The design responds to Ketzai’s educational tasks.

Keep improving from evidence

A working demo is a beginning. Use test cases and feedback to find where the feature is helpful, where it fails, and what users need to control. Expand capabilities when the evidence supports it, rather than adding tools simply because the agent can call them.

To assess AI opportunities in your business, explore my AI consulting and implementation services or tell me about your processes and needs. You do not need a defined solution.