AI Agentic Workflows
for Product &
Project Managers
Learn how to design practical AI agents that support product discovery, backlog refinement, prioritization, retrospective insights, project tracking, stakeholder communication, and decision-making workflows.
What makes this course different?
- Hands-on agentic workflow design using real Agile and product scenarios.
- Learn the difference between prompts, workflows, and AI agents.
- Build examples for user stories, retrospectives, risk tracking, and project updates.
- Designed for non-programmers and technology-aware leaders.
Build AI agents that support real product and project work
This course helps Product Managers, Product Owners, Project Managers, Scrum Masters, Agile Coaches, and delivery leaders understand and design agentic AI workflows that go beyond simple prompting.
Product discovery and backlog intelligence
Use agentic workflows to analyze customer feedback, create user stories, check INVEST, suggest acceptance criteria, and identify product opportunities.
Project visibility and risk insights
Design agents that summarize project updates, detect blockers, highlight risks, and generate stakeholder-ready status summaries.
Retrospective and team health insights
Analyze retrospective feedback, detect recurring trends, generate action items, and create facilitator-ready summaries.
By the end of this course, participants will be able to
Understand Agentic AI
Explain the difference between a prompt, a workflow, a tool-using assistant, and an AI agent.
Design Agent Workflows
Map state, nodes, edges, tools, routing, and outputs for product and project management scenarios.
Apply to Agile Use Cases
Build agentic workflows for backlog refinement, prioritization, retrospectives, risk reviews, and status reporting.
Evaluate AI Output
Review AI-generated recommendations using human judgment, ethics, transparency, and context awareness.
Suggested course curriculum
This structure can be delivered as a focused workshop, a half-day session, or an extended hands-on program.
Foundations of Agentic AI
What agents are, how they differ from chatbots, and where agentic workflows fit in product and project management.
From Prompting to Workflows
Move from one-shot prompts to structured workflows with state, nodes, decisions, and repeatable outputs.
Product Manager Agent Examples
Customer feedback analysis, product discovery support, user story generation, acceptance criteria, and prioritization support.
Project Manager Agent Examples
Status summary generation, blocker detection, risk analysis, stakeholder updates, meeting-note extraction, and follow-up tracking.
Hands-on Agentic Workflow Build
Participants design a practical agent workflow using a real scenario such as backlog refinement or retrospective insights.
Ethics, Governance, and Human Oversight
Explore bias, privacy, accountability, hallucinations, tool boundaries, and responsible use of AI in delivery teams.
Example workflows covered
- User story quality agent: INVEST, AC, split, estimate, recommendation.
- Retrospective insights agent: theme clustering, trends, action items.
- Project status agent: updates, blockers, risks, next steps.
- Product discovery agent: feedback patterns, opportunities, hypotheses.
- Stakeholder communication agent: summary, tone, audience adaptation.
Participants leave with reusable assets
- Agent workflow canvas
- Prompt templates for product and project workflows
- Backlog analysis sample
- Retrospective insights sample
- AI governance checklist
- Next-step plan for applying agents at work
Who should attend?
| Role | How this course helps |
|---|---|
| Product Managers / Product Owners | Use AI agents for discovery, feedback analysis, backlog refinement, story quality, and prioritization support. |
| Project Managers / Program Managers | Create workflows for status reporting, risk detection, meeting summaries, dependencies, and stakeholder communication. |
| Scrum Masters / Agile Coaches | Support team health, retrospectives, impediment tracking, coaching insights, and continuous improvement. |
| Delivery Leaders / Transformation Leads | Understand how agentic AI can improve visibility, decision-making, and responsible adoption across teams. |
Course format
Recommended duration
Half-day, one-day, or multi-session format depending on depth and hands-on practice.
Delivery mode
Virtual live workshop or corporate private training with practical exercises and facilitated discussion.
Prerequisites
No coding expertise required for conceptual track. Basic Python/Jupyter familiarity is useful for the hands-on technical track.
Bring Agentic AI into your product and project workflows
Designed for professionals who want to move beyond generic prompting and build practical, responsible, workflow-driven AI use cases.
Interested in this course?
Contact MSAgileMed for upcoming public batches, corporate workshops, or custom learning paths.
Enquire Now