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Agentic PDLC Tutorial: Run AI Agent Projects, Step by Step

Short lessons on running software projects where an AI model does part of the work: what to decide, in what order, and how to know you got it right.

Free · 55 lessons · by Akash Das · Read it on the site, with live diagrams ↗

Tip

This free tutorial teaches the agentic PDLC, a four-phase lifecycle (P0 Frame, P1 Design & Spec, P2 Build & Prove, P3 Run & Learn) for products in which a model drafts, decides or acts, in five-to-ten-minute lessons. It works with whatever method you already use, including AWS AI-DLC, the BMAD Method, spec-driven development and Scrum. The home page and the method page call it the SkyWays PDLC; the two names mean the same lifecycle.

Each role's part in the four phases is drawn on the method page.

Start where you are

If you are… Start with Because
New to agentic delivery What is the agentic PDLC? Everything else assumes the four phases
A product manager For product managers The pain, the autonomy and the bar are yours
A programme or delivery manager For programme managers The board, the cadence and the review queue
A solution architect For solution architects Which steps may guess, and where the caps live
An engineer or engineering lead For software engineers Bolts, the harness and the merge gate
A forward-deployed engineer The forward-deployed engineer guide Frame, Deliver, Evolve: twelve steps, each with its template
In QA For QA "It works" becomes proof that it works
In DevOps or platform For DevOps and platform The gateway, the flags and the rollback
A sponsor For business sponsors Two numbers, on one line, every cycle
An executive For executives Four decisions nobody else can make
Leading the change Rolling it out in 90 days One feature, five phases, one trap each
Wanting practice The SkyWays case study Ninety days, thirteen episodes, failures left in
Preparing for an interview Six answer frameworks Then the bank for your role, with strong answers
Curious about forward-deployed work What is an FDE? The role, its three stages, and who works beside it
Wondering why these projects go wrong Why agentic AI projects fail The failure modes are new, and quiet
About to write a spec The eight-field spec The step comes with its template
About to launch Shadow, then five percent The order a cut-over goes in
Asked for a business case The value line The arithmetic is worked
Running a workshop 37 scenarios Twenty sectors to choose from

What you will be able to do

By the end of the fundamentals you will be able to:

  1. Frame an AI feature as a measured pain, decide whether it needs a model at all, and set how much the machine may do on its own.
  2. Specify it so a coding agent, or an engineer who was not in the room, can build it without asking, with an acceptance bar per slice.
  3. Build and prove it in bolts, against a bar, with evidence a sceptic accepts.
  4. Run it: cut over safely, catch drift, and report what it saved beside what it cost.

The role, organisation and practice tracks then apply the same four phases to your job, to how a whole organisation adopts them, and to a full ninety-day case with twelve exercises; the interview track turns all of it into answers for AI product, FDE, GenAI, agentic and AWS roles.

How the lessons work

Every lesson has the same slots in the same order: the answer first, a picture, the problem stated plainly, the idea step by step, a problem to try, three takeaways, and its sources, each marked Borrowed, Adapted or Original to this manual. Why it is built that way.

The lessons are written once and published twice: here, with the live diagrams, and on the GitHub wiki, where they sit beside the manual's full reference pages.

Every lesson, in order

What the agentic PDLC is, in one sitting, and how to get the most out of the lessons that follow., 2 lessons, about 17 minutes.

  1. What Is the Agentic PDLC? The P0 to P3 Framework Explained, 10 min
  2. How This Tutorial Works, and Why Every Lesson Looks the Same, 7 min

The four phases, the one hard gate, the eight loops and the evidence that crosses each hand-off: the model every other track builds on., 9 lessons, about 67 minutes.

  1. Evolution of the PDLC: From Waterfall to Agentic Delivery, 8 min
  2. Why Agentic AI Projects Fail: 7 Failure Modes and Fixes, 7 min
  3. P0 Frame: How to Decide If an AI Agent Is Worth Building, 8 min
  4. P1 Design & Spec: Write a Spec an AI Agent Can Build From, 8 min
  5. The Hard Gate: The Hand-off You Can't Skip in AI Delivery, 7 min
  6. P2 Build & Prove: How to Build an AI Agent in Proven Slices, 8 min
  7. P3 Run & Learn: How to Run an AI Agent in Production Safely, 7 min
  8. The 8 Feedback Loops of AI Delivery and the 3 Teams Forget, 7 min
  9. The Evidence Pack: What Must Exist Before Each AI Hand-off, 7 min

AWS AI-DLC, AIDD, the BMAD Method and spec-driven development: what each one is, who coined it, where it sits on the four phases, and what it leaves for you to decide., 7 lessons, about 49 minutes.

  1. AI-DLC vs AIDD vs Agentic SDLC vs PDLC: What Each Term Means, 8 min
  2. What Is AI-DLC? AWS's AI-Driven Development Lifecycle, 7 min
  3. What Is AI-Driven Development (AIDD)? The Daily Craft, 7 min
  4. What Is the BMAD Method? Five Agents, One Build Loop, 7 min
  5. What Is Spec-Driven Development? Kiro and Spec Kit Explained, 7 min
  6. How AI-DLC, BMAD, Spec Kit and Scrum Fit One Lifecycle, 7 min
  7. How Much Process Does a Change Need? Sizing Agentic Work, 6 min

The practical core: the twelve steps, bolts and boards, cutting delivery time, reviewing AI-written code, setting and proving the bar, launching safely, cost, guardrails, governance, drift and postmortems., 13 lessons, about 85 minutes.

  1. How to Run an Agentic AI Project: A Step-by-Step Playbook, 7 min
  2. Bolts vs Sprints: Planning Work When AI Writes the Code, 6 min
  3. How to Set Up a Kanban Board for an Agentic AI Project, 7 min
  4. Cut Delivery from Months to Weeks: What Shrinks, What Won't, 7 min
  5. How to Review AI-Generated Code: By Risk, Not by Diff Size, 6 min
  6. How Accurate Does an AI Agent Need to Be? The 3-Step Answer, 7 min
  7. Is 82% Good Enough? How to Prove an AI Agent Meets Its Bar, 6 min
  8. Shadow Mode and Canary Releases for AI Agents, Step by Step, 7 min
  9. Why Your AI Agent Costs 4× the Estimate, and How to Fix It, 7 min
  10. AI Agent Guardrails That Hold: Why a Prompt Is Not a Control, 7 min
  11. AI Governance Gates That Don't Slow Delivery: The Five Gates, 6 min
  12. AI Drift: How to Catch the Defect With No Error Message, 6 min
  13. Postmortems for AI Incidents: Find the Missing Control, 6 min

5 · 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.

  1. The Agentic PDLC for Product Managers: What Changes Monday, 6 min
  2. Agentic PDLC for Program Managers: Boards, Bolts, Reporting, 7 min
  3. Agentic PDLC for Solution Architects: Authority and Topology, 7 min
  4. The Agentic PDLC for Software Engineers: Build With Agents, 6 min
  5. AI-DLC and AIDD for Forward-Deployed Engineers: Field Guide, 7 min
  6. The Agentic PDLC for QA: How to Test Probabilistic Software, 6 min
  7. The Agentic PDLC for DevOps and Platform Teams, 6 min
  8. The Agentic PDLC for Business Sponsors: Ask for Two Numbers, 6 min
  9. Agentic AI for Executives: Four Decisions Only You Can Make, 7 min

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.

  1. How to Structure Teams for Agentic AI: Roles, Pods and Seams, 6 min
  2. How to Measure AI Productivity Without Fooling Yourself, 7 min
  3. AI Maturity Model: Six Controls You Can Test in Ten Minutes, 7 min
  4. How to Roll Out Agentic AI Delivery: A 90-Day Plan, 8 min

7 · 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.

  1. Agentic AI Case Study: SkyWays, 90 Days from Pain to Proof, 7 min
  2. Agentic Delivery Cadence: What Runs Daily, Weekly, Quarterly, 7 min
  3. Agentic AI Workbench: Practise 90 Days of Delivery Decisions, 7 min
  4. Agentic PDLC Exercises: 12 Problems with Worked Answers, 9 min

What a forward deployed engineer does, six frameworks for answering AI interview questions, and deep question banks with strong answers for AI product managers, forward deployed engineers, GenAI engineers, agentic AI engineers and AWS roles., 7 lessons, about 73 minutes.

  1. What Is a Forward Deployed Engineer? The FDE Role, Explained, 9 min
  2. How to Answer AI Interview Questions: Six Frameworks, 8 min
  3. AI Product Manager Interview Questions, with Frameworks, 11 min
  4. Forward Deployed Engineer Interview Questions and Answers, 11 min
  5. GenAI Engineer Interview Questions: RAG, Evals and Cost, 11 min
  6. Agentic AI Engineer Interview Questions and Answers, 11 min
  7. AWS Generative AI Interview Questions: Bedrock, AgentCore, 12 min

Also here: the playbook's full reference, The Agentic PDLC, the role pages, the journeys, the how-tos, Formulas and the Glossary. Every lesson links into them where you need the depth.

✏️ This page is generated from site/content/learn/start-here.md: 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