Building & Scaling Agentic AI Products
Lead an agent product from investment decision to operating service. Design authority and recovery, build an evaluation program, defend pricing and adoption, and present a coherent plan across product, security and finance.
Opening lesson: Write an AI product strategy that tells teams what to stop
Syllabus
Open a module to choose a lesson. Access is shown beside each title.
01Set direction3 lessons · 50 min
Write an AI strategy teams can act on, fund AI work as a portfolio of staged bets, and set the quality, safety and cost bars every team uses.
02Design agent work4 lessons · ~1.5 hours
Choose autonomy, durable state, memory and tool contracts for an agent that operates inside a real organization.
03Build the operating system3 lessons · 49 min
Hire and grow AI product managers, run a rhythm for model changes and incidents across teams, and govern by risk tier so review effort matches risk.
04Operate the agent product5 lessons · ~1.5 hours
Evaluate completed work, maintain useful data, price outcomes and turn a pilot into a governed operating service.
05Make the organization work3 lessons · ~4.5 hours
Choose shared infrastructure, agree research commitments and defend an agent operating proposal across functions.
06Lead in the loop4 lessons · ~4 hours
Answer leadership-round prompts with a position and a portfolio, present an AI roadmap to executives, tell leadership stories that show judgment, and run a director-level mock loop.
About this course
A senior PM owns more than a feature recommendation. The agent must have appropriate authority, the review queue must be staffed, the price must cover the work, and somebody must know what to do when a task stops halfway through a purchase.
This course follows Corvane, a fictional HR software company with 1,200 business customers and six product teams. You set its AI strategy and develop a bounded onboarding coordinator. Every case figure is a teaching assumption. Published product stories and protocol or regulatory references appear in dated, attributed field notes.
From strategy to operation
Begin with investment choices and shared quality bars. Then design autonomy, durable task state, memory and tool contracts. The operating sections cover trajectory evaluation, data rights, pricing, adoption and governance across deployments. Finally, choose what the platform should own and how research results become product commitments.
The cases include both a small first team and coordination across several teams. Scale changes ownership and investment scope; it does not remove the need to inspect failures and understand the customer workflow.
Defend one coherent proposal
The four-hour project supplies a board request, staffing limits, costs and an imperfect quality run. You produce a strategy page, architecture, permission matrix, evaluation charter and pilot decision. Reviewers challenge the same proposal from product, security, finance and operations. The arithmetic deliberately exposes a capacity constraint you must resolve.
The closing director-level mock switches to Clinova, a fictional clinic-management product serving the UK and India. That change tests whether you can adapt your judgment to a higher-consequence domain without reusing Corvane's answers.
Study and review
| Plan | Suggested sequence |
|---|---|
| Four weeks | Direction and architecture; operating system; agent operations; organization, project and leadership mock |
| Eight weeks | One section each week, with separate weeks for project defense and mock revision |
| Twelve weeks | Add a second workflow, an incident tabletop and another executive presentation |
Write the decision before opening the reference reasoning. Ask a peer to challenge the assumption most likely to change it. Every lesson includes a diagram and self-check; the project works with paper or a document and requires no live customer systems.
Two lessons are public and two more are free with sign-in. Premium covers the remaining material. Times include suggested practice and project work. These are self-assessments, not professional certification or employer-endorsed rubrics.
When you finish, you can
- Choose an AI portfolio and define the work the organization will stop.
- Design action authority, durable execution, memory and tool contracts for an agent product.
- Operate evaluations, data improvement, pricing and pilots with explicit owners and evidence.
- Defend an agent operating proposal and a director-level mock on a different company.
BEFORE YOU START · Experience owning a product area or leading teams, plus working knowledge of AI product design and evaluation. The foundations course supplies the technical prerequisites; the interview course provides additional timed practice.