Build evidence-led AI workflows across product discovery, specification, prototyping, deployment and continuous improvement.
| Course detail | Information |
|---|---|
| Course code | C840 |
| Programme | Non-WSQ |
| Duration | 2 days / 15 instructional hours (9:30am – 5:30pm daily) |
| Registration | View course details and register |
This course develops practical product-management capability for agentic AI. Learners evaluate builders on a common task, turn research evidence into product decisions, define bounded agent roles and handoffs, create evaluation sets, optimise a prototype, and recommend a governed deployment posture.
Slides stay conceptual and visual; detailed procedures, troubleshooting and checks are in the Learner Guide and the activity folders.
- Evaluate agentic AI builders for product research and development, comparing strengths, limitations, resources and skills.
- Apply optimisation techniques to improve the efficiency, quality and control of agentic product-development workflows.
- Evaluate the benefits and trade-offs of deploying agentic AI-powered product solutions and recommend a governed path forward.
- Evaluating Agentic AI Tools for Product Research and Development
- Building and Optimising Agentic Product Development Workflows
- Deploying and Evaluating Agentic AI-Powered Product Solutions
- Activity 01 — Frame a Product Opportunity
- Activity 02 — Compare Agentic AI Builders
- Activity 03 — Build a Research Evidence Pack
- Activity 04 — Create Persona and JTBD Map
- Activity 05 — Design an Agent Workflow Canvas
- Activity 06 — Create an AI-Ready PRD
- Activity 07 — Engineer a Prompt and Evaluation Set
- Activity 08 — Run a Prototype Experiment
- Activity 09 — Select a Deployment Posture
- Activity 10 — Conduct a Risk and Control Review
- Activity 11 — Design KPI and Feedback Dashboard
- Activity 12 — Deliver a Governed Product Recommendation
Each activity is self-contained and includes a detailed guide, a PDF copy, brief.yaml, an agent prompt, synthetic CSV/JSON data, an expected-output contract, an evidence checklist and a deterministic verifier.
- Slide deck (PPTX) · PDF — 199 slides, v1.0
- Lesson Plan (DOCX) · PDF
- Learner Guide (DOCX) · PDF · Markdown
activities/— twelve learner-safe, self-contained hands-on activitiestools/— the scripts that rebuild this courseware from its source set
This public repository holds learner-facing courseware only. Source references, credentials and QA renders are not published.
Tertiary Infotech Academy Pte. Ltd.
UEN 201200696W