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Tutorial Practice
The SkyWays case study in thirteen episodes, the operating rhythm from daily to quarterly, the workbench, and twelve exercises with worked answers.
4 lessons · about 30 minutes · A case, a workbench, twelve problems
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 | Agentic AI Case Study: SkyWays, 90 Days from Pain to Proof | A worked agentic AI case study: a fictional airline's rebooking assistant in thirteen episodes, from 31 requirements on day 1 to two numbers on day 90. | 7 min |
| 2 | Agentic Delivery Cadence: What Runs Daily, Weekly, Quarterly | The operating rhythm of an agentic AI project: what runs every day, on every change, every week, every cycle and every quarter, and who owns each check. | 7 min |
| 3 | Agentic AI Workbench: Practise 90 Days of Delivery Decisions | A free, browser-only agentic AI workbench: 13 dated episodes, 9 decision simulations, 17 prefilled tools and an evidence pack that downloads as one file. | 7 min |
| 4 | Agentic PDLC Exercises: 12 Problems with Worked Answers | Twelve agentic AI delivery exercises with worked answers, three per phase: AI-fit, value, gates, bars, chains, bounds, cases, queues, bills, drift, controls. | 9 min |
← Teams and organisation · All tracks · Interviews and careers →
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.
This wiki is the connective layer. The repository is canonical and versioned; these pages are the cross-surface maps and the notes that change too often to live in a reviewed file. Anyone can edit — see Editing this wiki.
▶ Start here · 55 lessons, on the site ↗
- Getting started · 2
- Agentic PDLC fundamentals · 9
- Methods decoded · 7
- Running delivery · 13
- By role · 9
- Teams and organisation · 4
- Practice · 4
- Interviews and careers · 7
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…?
The 16 modules
- Agentic Foundations
- LLM Intuition and the AWS Bridge
- Amazon Bedrock Essentials
- Amazon Bedrock Agents
- Agent Builder, Knowledge Bases and Guardrails
- The Agent Loop: No Framework to Strands
- Strands Foundations: Tools, Memory and MCP
- Multi-Agent Patterns with Strands
- LangChain and LangGraph
- LLM Memory Mechanics
- RAG, OpenSearch and LiteLLM
- Amazon Bedrock AgentCore
- A2A and A2UI: Agent Interoperability
- Agentic QA and Evaluation
- End-to-End Production Pipeline
- Agentic Product Lifecycle
Learning paths · Field guide
Study Plans
Eight sessions
Living notes
Error Index
Community Answers
Field Notes
Model & Region Notes
Cost Log
Scoreboard 🧪
The project
Roadmap
Editing this wiki
Maintainer Runbook
📘 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 →