Jean Leah

PhD Engineer & AI Executive (Dell, Lowe’s, Texas A&M)

Questions to Ask Before You Build an AI Agent

Distinguish work best handled by humans, deterministic automation, AI assistance, or agentic execution.

Identify the right work for agents

Evaluate a workflow across ambiguity, variability, tool use, iteration, economic value, and consequence of failure.

Apply the Agentic Fit Test

 Ability  to classify an opportunity as Automate, Assist, Agentify, Keep Human, or Kill and explain why.

Make a clear investment decision

Turn AI into your competitive advantage—automate, optimize, and lead smarter with agentic workflows.

Cohort 19

UC Berkeley Instructor | ex CIO & CPO 

UC Berkeley

Microsoft

James Gray

UC Berkeley AI Instructor | Former CIO/CPO | Trained 5,000+ Leaders

Cohort 18

Cohort 20

You've evaluated, approved, and strategized AI. You've built bits and pieces. You haven't built systematically. Your team experiments with prompts; nobody ships repeatable workflows. You don't have a method for turning your highest-value work into systems that run on their own. The builders are pulling ahead — and the gap widens by the quarter.In 4 weeks, you'll:✅ Apply a proven 7-step lifecycle to build, ship, and manage any AI workflow✅ Shift from chatting to building — manage AI building blocks as files you own✅ Build a portfolio of evaluated agent skills that capture your expertise and run anywhere✅ Ship deterministic and guided AI workflows that drive real business impact✅ Build single and multi-agent autonomous systems for your highest-value work✅ Run a living AI Registry that makes your portfolio discoverable and sustainableThis isn't theory — it's a hands-on builder methodology from training 5,000+ leaders at UC Berkeley and a decade at Microsoft. Rated 4.8/5. Intimate cohorts, 1:1 coaching, lifetime access. We build together — not lectures.The course focuses on concepts and frameworks that can be applied to multiple AI platforms - OpenAI, Claude, Gemini, and M365.

AI transformation belongs to the builders. The rest get left behind.

Apply a 7-step lifecycle to build, ship, and manage any AI workflow

Shift from chatting to building — manage AI building blocks as files you own

Build a portfolio of agent skills that capture your expertise and run anywhere

Build deterministic and collaborative AI workflows powered by skills

Build single and multi-agent autonomous workflows for your highest-value work

Build a living AI Registry that makes your work discoverable and sustainable

The course extensively utilizes AI assistants, such as ChatGPT, Claude, and Gemini. Some lessons are unique to these tools.

You have a paid subscription to one or more: ChatGPT, Claude, Gemini

The course does not require coding experience, but we will use AI code editors (Cursor/VS Code) to build and refine prompts

No coding experience

This cohort experience is for people who learn best in small groups and value personalized feedback from an experienced instructor.

Learning in a small, supportive cohort

Leaders and professionals who believe in “learning by doing” by using cutting-edge tools and building AI solutions&nbsp;hands-on.

Leaders who must understand the latest AI capabilities to&nbsp;drive an AI-powered business strategy credibly.

Professionals who must boost their AI technology acumen to strengthen collaboration and influence with technical stakeholders.

Bring one real workflow and leave with clearer roles for AI and people to test 

Human-AI Work & Systems Advisor

Joyce Ventures

University of Wisconsin-Madison

Center on PBIS

Joyce White Nelson 

Systems-Change Advisor | Human Judgment & AI-Assisted Work | Founder, iQuoteHero

Founding Live Workflow Lab — September 2026

AI can help you move faster and sound 'good'. But if the workflow is unclear, speed and false positives can also create rework, weak review, lost context, generic output, or confusion about who owns the result.In this working Lab, you will bring one real AI-assisted workflow, decision, client deliverable, communication, or recurring task. You will map how the work currently happens, identify what AI should assist with, decide what needs human judgment and verification, and redesign with clearer roles for you, AI, and anyone else involved.Send me your workflow, decision, or use case ahead of time. That helps me shape examples and guidance around real situations.You will leave with:<ul><li>A completed Human + AI Workflow Map.</li><li>Clear decisions about what to delegate, retain, verify, or change.</li><li>Defined accountability and judgment points.</li><li>One practical experiment to test in your work.</li><li>A reusable method for evaluating future AI-assisted workflows.</li></ul>After the workshop, I will follow up by email or brief review call to help you examine what happened when you tested the workflow. We will look at what improved, what created friction, and what to keep, revise, or stop.

Redesign one real AI-assisted workflow so the Human/AI roles are defined and make  human judgment, accountability, or expertise visible.

 Map one workflow as it works today

Decide where AI belongs

. Protect human judgment and accountability

 Identify the most important risk or rework point

 Design one improved workflow experiment

Workflow to measure the outcome 

Experienced entrepreneurs and founders Already using AI and want clearer judgment, better workflows, and less hidden rework.

Consultants, coaches, and advisors Use AI in client work but want to protect quality, credibility, voice, and professional responsibility.

Leaders, educators, and team facilitators Need ways to decide where AI helps, where humans stay accountable, and people keep learning.

See your venture clearly, find what is holding it back, and choose the next move—so AI creates leverage instead of more work.

Systems Architect · Founder, Sysdom AI

THE WORLD BANK GROUP

Carnegie Mellon University

Han Kay

Systems Architect · Carnegie Mellon (MISM) · Triple Master's (MBA, MS) · 30+ yrs

Cohort 1

AI gives founders two possible futures. It can multiply company capacity—or multiply the coordination work returning to the founder. If you're building an AI-native or agentic company, adding agents is the easy part. The harder problem is turning their output into leverage without becoming the integration layer for every handoff, decision, and next move. The missing layer is the system the agents operate inside. That is Systems Intelligence.This four-week working lab turns Systems Intelligence into founder practice. Across four focused 90-minute sessions, you will map your venture, identify the active constraint beneath the surrounding noise, commit one bounded next move, and complete a founder-directed operating cycle.Sysdom.ai is the included, supported working environment. It organizes venture context across the Product/Service, Customer, Cash, and Skills Engines, preserves evidence, and records the chosen move. You remain responsible for direction, consequential decisions, and approval boundaries.Leave with a clearer venture state, explicit decision boundaries, one completed cycle, and a repeatable way to increase agent capacity without increasing founder coordination.

AI amplifies everything. Including what’s broken.

See your venture as one system

Build an evidence-backed venture map

Find the constraint behind the noise

Choose one bounded next move

Set founder and agent decision boundaries

Start a guided operating cycle

Bring research, a prototype, operating signals, prior decisions, or a serious plan. No website, revenue, or technical setup required.

Bring one real venture—and some evidence

Founders building a venture who feel complexity outpacing their mental model. You have pieces but no unified operating system.

Founders who hit their first real bottleneck and realized working harder isn't solving the structural problem underneath.

Founders whose AI tools and agents produce more output while every important decision and handoff still routes back through them.

A practical workshop on evaluating AI agents before launch

Founder & CEO | Ex-Adobe, WF, eBay | HBS

Adobe

Wells Fargo

eBay

Ganesan Anand

Founder, Formerly AI Product & Tech Leader (Adobe, Wells Fargo, eBay) · HBS alum

Next Cohort

<blockquote>Move your AI features from a vibe check to a value check. “Did the agent do the right thing? Would I catch it if it didn’t?” Every team shipping AI features is asking some version of these questions without a reliable way to answer them. An agent doesn’t just say the wrong thing. It does the wrong thing, and teams often find out from a user.Typical AI training falls into two traps: abstract theory that never leaves the slide deck, or disconnected techniques that stop short of a production-ready system. This workshop bridges that gap. I build enterprise infrastructure for AI evaluation every day, and I’ve seen that real adoption only happens when teams can trust their AI to perform consistently.You’ll learn a repeatable method for applying Responsible AI in practice, with a focus on reliability and safety. You’ll learn which metrics matter for your product, identify the edge cases that undermine trust, define how the product should respond, and prove it works before customers see a problem.</blockquote>

Learn a repeatable evaluation framework for building and releasing reliable AI features.

Know exactly what to measure

Find failures before your users do

Get a pass/fail read before release

Keep quality from slipping after launch

Access to leading commercial and open-source AI models is provided during the session for the eval exercises. Nothing to set up or pay for.

Model access is provided

Come ready to dig into how AI features fail and how to catch it. That mindset is all it takes.

Curiosity about AI quality

The session is live and remote, so a solid connection matters. Everything runs in-browser, and the no-code evaluation platform is provided.

Stable internet

AI engineers and product managers building, evaluating, or preparing to release AI features

Founders turning an AI prototype into a reliable product

Engineering and product leaders accountable for AI quality, safety, and release decisions

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Product managers who drive strategy, development, and launch of products.

Executives and senior leaders who set strategy, lead teams, and drive organizational outcomes.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Setting strategic direction, making high-stakes decisions, and translating vision into execution.

Leading organizational AI adoption — change management, strategy, and building AI-ready teams.

Questions to Ask Before You Build an AI Agent

What you'll learn

Why this topic matters

You'll learn from

Jean Leah