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Tutorial Teams and Organisation

github-actions[bot] edited this page Oct 2, 2026 · 7 revisions

Teams and organisation

How to structure teams, measure productivity without fooling yourself, assess maturity by controls rather than tools, and roll the method out in ninety days.

4 lessons · about 28 minutes · Structure, measures and rollout

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 How to Structure Teams for Agentic AI: Roles, Pods and Seams No new roles, none disappears, but two boundaries move. How to organise product teams, a platform team and governance for agentic AI, and where the seams go. 6 min
2 How to Measure AI Productivity Without Fooling Yourself Measure AI productivity against a baseline taken before the pilot, and report the saving beside the spend, review hours and re-runs, with a worked example. 7 min
3 AI Maturity Model: Six Controls You Can Test in Ten Minutes An AI maturity model that counts controls, not tools: six controls, each proven by a test you run in minutes, and how to pick the next one to build. 7 min
4 How to Roll Out Agentic AI Delivery: A 90-Day Plan A 90-day plan for rolling out agentic AI delivery: pick for provability, write the artefacts, build in slices, shadow beside the humans, report two numbers. 8 min

← By role · All tracks · Practice →

Teaching it? Session 7 · Govern and scale 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 →

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