Understand the request
Learn how the team works today and agree what success looks like.
All work01 02 03 04 05 06
CASE STUDY / AGENTIC AI / ENTERPRISE DEPLOYMENTProduction practice (not a single system)
From Copilot Prompt to Enterprise-Ready Agent
How I take a business request and turn it into a governed, tested, adopted Copilot Studio agent.
MY ROLE
I build and deploy Microsoft Copilot Studio agents for business teams at Duke Energy, working with stakeholders from the first request through release and adoption.
PROJECT STATUS
Production practice (not a single system)
FOCUS
Agentic AI / Enterprise Deployment · Enterprise AI
01 / BUSINESS CHALLENGE
Start with the problem.
Business teams have good ideas for AI agents, but turning a prompt into something a regulated company can safely deploy takes more than building it: it needs clear requirements, governance review, testing, and adoption support.
02 / SOLUTION APPROACH
My role
I build and deploy Microsoft Copilot Studio agents for business teams at Duke Energy, working with stakeholders from the first request through release and adoption.
My contribution
- Agents for the power operations side of the business, including transformer analytics, transformer details and readings, and autonomous workflow, plus the agents behind the logistics and vendor applications described in Project 2.
04 / IMPLEMENTATION
The process
Build
Create the agent with a defined scope, the tools it may use, and a person confirming key actions.
Governance review
Work with security, compliance, and IT reviewers on who can use the agent, what data it touches, and what it logs.
Test
Test with the business users as the agent moves from development to testing to user acceptance to production, and record the results.
Release and adoption
Roll out, show people how to use it, and answer questions.
Monitor and improve
Review real usage and feedback, fix what is failing, retest, and release updates through the same steps.
The process
Understand the requestBuildGovernance reviewTestRelease and adoptionMonitor and improve
WHEN IT DOESN’T WORK
When it doesn’t work.
If users don't like an agent after release, I find the cause (wrong problem, weak answers, harder than the old way, or low trust), fix it, and retest. If it still isn't helping, I retire it instead of forcing adoption.
07.5 / TRUST & GUARDRAILS
How the system fails safely.
Human confirmation
Human confirmation before key actions.
Agent activity log
A log of what each agent does.
Approved access
Access limited to approved users.
Governance review
Governance review before release.
08 / OUTCOMES
What the work demonstrates.
A repeatable way to take a business request to an agent that is governed, tested with real users, and adopted by the team: clear scope, security and compliance review, user acceptance testing, and support after release.
Enterprise Knowledge AI Assistant
05 /A CONVERSATION AWAY
Let’s build
what’s next.
Interested in enterprise AI, agentic systems,
or intelligent automation? Let’s connect.