How DDD works in an agentic-AI development lifecycle?

Scenario

Build an Order Management system with:

• order lifecycle

• payments

• shipment

• cancellations

• refunds

We’ll map roles → inputs → artifacts → outputs → consumers.

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1) Roles in AI-era DDD

We’ll use these roles:

• Architect

• Domain Designer

• Developer

• AI Coding Agent

• QA / Validator AI

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2) Step-by-step lifecycle with artifacts

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Step 1 — Architect defines domain boundaries

Architect input

• business scope

• enterprise architecture

• system landscape

• integration needs

Architect produces

Bounded Context Map

Example:

Order Management
Payment
Shipping
Customer
Catalog

Relationships:

Order → uses Payment
Order → triggers Shipping
Order → reads Catalog

Who consumes

• Domain Designer

• AI Agent

• Developers

👉 This tells AI: “don’t mix payment logic into order.”

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Step 2 — Domain Designer defines ubiquitous language

Designer input

• workshops with business

• policies

• terminology

• lifecycle rules

Designer produces

Domain Glossary

Example:

Order: confirmed customer purchase request
OrderItem: purchasable line in order
PaymentAuthorization: reserved funds approval
Shipment: physical dispatch record
Cancellation: customer or system termination
Refund: money returned after capture

Format given to AI

concepts:

  Order:
    description: confirmed purchase
  PaymentAuthorization:
    description: reserved funds approval

Who consumes

• AI Agent

• Developers

• QA

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Step 3 — Designer defines lifecycle & invariants

Designer produces

State model

Draft → Placed → Paid → Shipped → Completed
                ↘ Cancelled

Invariants

- Cannot ship unpaid order
- Cannot cancel shipped order
- Refund requires payment

AI input format

aggregate: Order
states:
  - Draft
  - Placed
  - Paid
  - Shipped
  - Completed
  - Cancelled

rules:
  - ship requires Paid
  - cancel forbidden after Shipped

Who consumes

• AI Agent

• Developers

• QA

---

Step 4 — Architect defines events between contexts

Architect produces

Domain Events

OrderPlaced
PaymentAuthorized
OrderPaid
OrderShipped
OrderCancelled
RefundIssued

And routing:

OrderPlaced → Payment
OrderPaid → Shipping
OrderCancelled → Payment (refund)

Who consumes

• AI Agent

• Integration teams

• Developers

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Step 5 — AI Agent generates domain model & services

AI input

• contexts

• glossary

• states

• invariants

• events

AI produces

Aggregate

Order {
  id
  items
  state
  paymentStatus
}

Methods

place()
markPaid()
ship()
cancel()
refund()

Guards (from invariants)

ship():
  require state == Paid

Events

emit OrderPaid
emit OrderShipped

Who consumes

• Developers

• QA

• Architect

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Step 6 — Developer reviews & refines

Developer input

• AI-generated domain code

• invariants

• business edge cases

Developer tasks

• edge rules

• performance concerns

• persistence mapping

• error handling

Developer produces

• refined domain logic

• APIs

• integration adapters

Who consumes

• AI QA

• System tests

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Step 7 — AI QA validates domain correctness

AI checks:

• invalid transitions

• rule violations

• missing guards

• event consistency

Example test generated:

Given unpaid order
When ship
Then error "Unpaid order"

Who consumes

• Developers

• CI pipeline

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3) Artifact flow summary

Architect → Context Map → AI + Designer + Dev
Designer → Glossary → AI + Dev
Designer → States/Invariants → AI + Dev
Architect → Events → AI + Dev
AI → Domain Model → Dev
Dev → Refined System → QA AI
QA AI → Tests → Dev

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4) What each role does in AI-era DDD

Architect

Focus: boundaries & interactions

Produces:

• contexts

• events

• integrations

Does NOT:

• design classes

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Domain Designer

Focus: meaning & rules

Produces:

• glossary

• lifecycle

• invariants

This role becomes MORE important with AI.

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Developer

Focus: correctness & engineering

Does:

• validate AI output

• refine logic

• implement infra

• performance

Less time on:

• boilerplate

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AI Coding Agent

Focus: generation

Consumes:

• domain specs

Produces:

• aggregates

• services

• APIs

• tests

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AI QA Agent

Focus: verification

Consumes:

• invariants

• lifecycle

Produces:

• tests

• violations

• edge scenarios

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5) How DDD artifacts look in practice

Example combined spec given to AI:

context: OrderManagement

aggregate: Order

states:
  Draft
  Placed
  Paid
  Shipped
  Completed
  Cancelled

rules:
  - ship requires Paid
  - cancel forbidden after Shipped
  - refund requires Paid

events:
  OrderPlaced
  OrderPaid
  OrderShipped
  OrderCancelled
  RefundIssued

This replaces hundreds of lines of manual modeling.

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6) Key shift from traditional DDD

Before:

Designer → UML
Dev → Code manually

Now:

Designer → semantic spec
AI → code
Dev → validate

DDD moves from diagrams → machine-readable semantics.

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7) Why this works well with AI

AI is strong at:

• structure generation

• boilerplate

• mapping rules to code

Humans remain better at:

• meaning

• boundaries

• policies

• intent

DDD defines exactly those.

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Final takeaway

In agentic-AI development:

• Architect defines contexts & events

• Designer defines language & invariants

• AI generates domain implementation

• Developer validates & engineers

• AI QA verifies rules

DDD becomes the semantic foundation layer that guides AI agents to produce correct systems.