How to build an enterprise AI agent around a real workflow

1. Name the problem and the person who owns it

An AI agent is software that can use information and tools to work toward a goal. In a company, that goal needs a practical boundary. “Help an employee submit a complete support request” is easier to build and test than “handle operations.”

I begin by asking who needs the result, what slows them down and what a successful handoff looks like. In my test-fitting support pilot, the job is helping employees find approved instructions or submit a structured request. Engineering recommendations belong to the design team.

Write a one-sentence job, name its owner and record the current process. Measure how long a representative request takes and how much rework it needs. That becomes the baseline for evaluating the pilot, rather than a savings figure invented after the demo.

2. Map the work before writing the prompt

List the steps from request to completion. For each step, identify the person responsible, the required information, the system holding it and the decision that follows. Mark where a missing input should stop the process.

For support intake, the path might be: understand the request, check the relevant instructions, collect missing fields, show a summary, ask for confirmation, create the queue item and return its reference. Put human review before an external escalation.

This is process design. An AI-native workflow can bring context, connected tools, permissions and review together. Those pieces need to fit the work people actually do.

3. Decide which sources the agent can trust

Knowledge grounding means giving an answer a reliable basis in approved information. Retrieval-augmented generation, or RAG, finds relevant material and supplies it to the language model when it answers.

Create a source register with an owner, version and review date for each policy, procedure or training resource. Decide which source wins if a chat message conflicts with an approved operating procedure. Preserve the caller’s existing permissions when retrieving content.

Require source links for factual answers. If the relevant source is missing, stale or contradictory, the agent should explain the gap and route it to a person. A confident sentence is not evidence that the underlying fact is current.

4. Separate language, calculations and actions

Language models are useful for understanding requests, summarizing evidence and explaining options. Repeatable calculations should use validated rules or specialist software. A design-sizing result needs a calculation with traceable inputs; it should not be an estimate invented by a chatbot.

Define each tool’s inputs, output and permission scope. For example, a Microsoft Copilot Studio agent in Teams can classify a request and prepare a SharePoint list item. The workflow should validate required fields, ask for confirmation and report the actual saved reference.

Start with read-only answers and drafts. Add write actions when the workflow can distinguish a proposal, an approved action, a completed action and a failed attempt. Prevent duplicate submissions when someone retries after a timeout.

5. Put human decisions at explicit gates

Human-in-the-loop means a person has a specific decision to make and enough evidence to make it. Show the proposed action, its sources, important assumptions and the next destination before asking for approval.

In the test-fitting support pilot, requests enter an internal queue and a person reviews them before the external vendor process. The separately specified test-fitting workflow would also retain design-manager review. It is architecture work, not an already autonomous design service.

Write down what the agent must escalate, who receives it and what happens when nobody is available. Preserve the specialist systems and professional accountability that already govern consequential work.

6. Test scenarios, then expand the pilot

Agent evaluations, often called evals, check whether the system completes the right task using the right evidence and permissions. Include normal requests, incomplete inputs, conflicting sources, unauthorized requests, tool failures and attempts to override its instructions.

Score factual support, task completion, correct routing, confirmation before actions and recovery after failure. Use historical examples with known expectations where available. Simulation before deployment can exercise failures without calling production systems; it still needs validation against real use.

Pilot with a small group, inspect the corrections people make and compare results with the baseline. Add another action only when the earlier scope is reliable. Multi-agent orchestration becomes useful when distinct jobs need coordination, not simply because several agents sound more advanced.

How my education and experience inform this approach

I completed Agentic AI: Strategy, Applications and Organizational Impact through UC Berkeley Executive Education, Haas, and hold AWS Certified AI Practitioner. I connect that education with hands-on work in project context, decision support, executive communication and operational support.

The sequence above is my practical application of that learning and experience. It is not a reproduction of a course syllabus. My enterprise AI agent examples distinguish created agents, a published internal pilot and systems that are still specified or at proof-of-concept stage.

A one-minute recap

Watch the 60-second Addy short for an on-screen summary of the six steps. This edition uses written captions without narration. The enterprise AI agent stories show how the approach applies to project context, decisions, executive communication and internal support.

Questions

Do I need multiple agents for the first version?

Usually one bounded workflow is enough to learn from. Separate agents make sense when responsibilities, tools or review steps are genuinely distinct. Keep the coordination cost visible.

What should I measure before claiming business value?

Compare task completion, errors, rework, human review effort and time to a useful result against the current process. Report the sample and its limits. A published pilot is a stage of development, not proof of a particular return on investment.

Which technology should I choose?

Choose technology that fits the company’s identity, data access and operating systems. Copilot Studio, Teams and SharePoint fit one of my support workflows. Other environments may need a different model provider, retrieval layer, tool interface or orchestration framework.