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Tutorial By Role

github-actions[bot] edited this page Oct 2, 2026 · 8 revisions

By role

How each discipline works in the agentic PDLC (product, programme, architecture, engineering, forward-deployed, QA, DevOps, the sponsor and the executive): what changes, what is theirs, and what is not.

9 lessons · about 58 minutes · Your job, phase by phase

Every lesson opens on the site, where the pictures are live and the text is searchable: start this track ↗, or pick a lesson.

# Lesson What it covers Time
1 The Agentic PDLC for Product Managers: What Changes Monday What an AI product manager does in the agentic PDLC: measure the pain, decide if it is AI at all, set autonomy per action, derive the bar, report two numbers. 6 min
2 Agentic PDLC for Program Managers: Boards, Bolts, Reporting How a program or delivery manager runs agentic AI delivery: the board, the cadence, the decision log, the review queue, gate conditions and two numbers. 7 min
3 Agentic PDLC for Solution Architects: Authority and Topology What a solution architect decides in agentic AI: which steps may be probabilistic, how many agents, the authority budget, where caps live, and the records. 7 min
4 The Agentic PDLC for Software Engineers: Build With Agents How software engineers work in the agentic PDLC: a context file, story files, exact code first, gated tools, a harness that decides merges, and a bolt a day. 6 min
5 AI-DLC and AIDD for Forward-Deployed Engineers: Field Guide How a forward-deployed engineer runs AI-DLC and AIDD at a customer: measure their pain, have their risk owner sign the limits, build in their stack, hand over. 7 min
6 The Agentic PDLC for QA: How to Test Probabilistic Software How QA works in the agentic PDLC: the proof each step owes, golden sets by slice, lower bounds instead of scores, injection suites, shadow runs and drift. 6 min
7 The Agentic PDLC for DevOps and Platform Teams What DevOps and platform teams own in agentic AI: the landing zone, one model gateway, pinned versions, the harness as a required check, flags and rollbacks. 6 min
8 The Agentic PDLC for Business Sponsors: Ask for Two Numbers What a business sponsor owns in an agentic AI programme: funding past cycle one, the autonomy ceiling, what counts as evidence, and three reports to insist on. 6 min
9 Agentic AI for Executives: Four Decisions Only You Can Make The four agentic AI decisions nobody below an executive can make, four questions to ask every cycle, seven red flags, and a first thirty days with no budget. 7 min

← Running delivery · All tracks · Teams and organisation →

Teaching it? Session 8 · Practice and roles in the Cohort Kit turn this track into ninety-minute sessions.

✏️ This page is generated from site/content/learn/curriculum.py: an edit made here is replaced at the next sync.

🗺️ Navigate

🏠 Wiki Home

▶ Start

▶ Start here · 55 lessons, on the site ↗

  1. Getting started · 2
  2. Agentic PDLC fundamentals · 9
  3. Methods decoded · 7
  4. Running delivery · 13
  5. By role · 9
  6. Teams and organisation · 4
  7. Practice · 4
  8. Interviews and careers · 7

📗 Method and reference

The method
The Agentic PDLC
The Eight Loops
Gates & Governance
The Evidence Pack

The journey · end to end
Product manager
Solution architect
Engineering lead
QA lead
DevOps & platform

The method · by role
Product manager
Solution architect
Engineering lead
QA lead
DevOps and platform
Sponsor

How to
Run an NFR workshop
Design an agent on paper
Build, buy or borrow
Cut sprints into bolts
Review by risk band
Prove the bar
Control the token bill
Hold the security boundary
Run a missing-control postmortem

Reference
Mental Models
Glossary
Formulas & Calculators
Decision Trees
Scenario Library
Exercises & Answers
Anti-Patterns
Sources & Confidence
Where do I find…?


📚 Course and labs

The course companion · Labs

The 16 modules
  1. Agentic Foundations
  2. LLM Intuition and the AWS Bridge
  3. Amazon Bedrock Essentials
  4. Amazon Bedrock Agents
  5. Agent Builder, Knowledge Bases and Guardrails
  6. The Agent Loop: No Framework to Strands
  7. Strands Foundations: Tools, Memory and MCP
  8. Multi-Agent Patterns with Strands
  9. LangChain and LangGraph
  10. LLM Memory Mechanics
  11. RAG, OpenSearch and LiteLLM
  12. Amazon Bedrock AgentCore
  13. A2A and A2UI: Agent Interoperability
  14. Agentic QA and Evaluation
  15. End-to-End Production Pipeline
  16. Agentic Product Lifecycle

Learning paths · Field guide
Study Plans


🎓 Cohort kit

Cohort Kit · Session template

Eight sessions
  1. Kick-off
  2. Frame and spec
  3. Build, run and the loops
  4. Methods decoded
  5. Plan and review
  6. Prove and ship
  7. Govern and scale
  8. Practice and roles

💬 Community and maintenance

Living notes
Error Index
Community Answers
Field Notes
Model & Region Notes
Cost Log
Scoreboard 🧪

The project
Roadmap
Editing this wiki
Maintainer Runbook


📦 Elsewhere

📘 The manual · live
🛫 Ninety Days, the simulator
🧰 The workbench
👔 For leadership
🧠 Mental models
Templates · Prompts
Frameworks & acronyms
The repository · Setup

Pinned discussions
Discussion map — where to post what
Exercise & lab index
Simulator Arena — get a lab graded
Pitch an idea
All discussions →

Clone this wiki locally