This is Part 4 of the “AI-Powered Domain Engineering” series. Check out Part 1: Beyond Vibe Coding: Why AI Won’t Replace Software Engineers, Part 2: The Context Conundrum: A Shared Limit for Humans and AI, and Part 3: Domain-Driven Design: Streamlining AI-Powered Software Development. In this installment, we bridge theory to practice by designing AI agents that preserve architectural coherence — the “continuous narrative” that makes great software endure.
We’ve journeyed through the vibes and pitfalls of AI in software engineering. In Part 1, we mapped the zones where AI shines (and ghosts) amid growing complexity. Part 2 unpacked the context conundrum, showing how abstraction, functional programming, and Clean Architecture keep code manageable for both human and machine minds. Part 3 layered in Domain-Driven Design (DDD) as the strategic crown, turning these tools into a hierarchy that democratizes excellence with AI’s tireless help.
But here’s the rub: AI agents today excel at local tasks — generating code, fixing bugs, or refactoring snippets — but they often fragment the bigger picture. Without a unified “system narrative,” their outputs create cacophony, not harmony. Think of legendary projects like Linux or PostgreSQL: Their strength came from a core team’s mental model, ensuring every piece aligned. AI lacks this innate coherence, leading to architectural drift in domain-heavy work.
Enter architecture-aware AI agents: Specialized collaborators designed to amplify human oversight while embedding coherence into every interaction. Drawing from Clean Architecture’s layers and DDD’s bounded contexts, we’ll outline 12 principles for building these agents. Inspired by the Twelve-Factor App but tailored for AI, these form the Architecture-Coherent AI Development (ACA-D) framework — evolving from semi-autonomous assistance to a multi-agent partnership that maintains the system’s soul.
This isn’t about fully autonomous AI architects (that’s still Sci-Fi). It’s about practical design that lets AI handle implementation while humans steer the vision, ensuring domain-heavy projects stay evolvable and robust. Let’s break it down with principles, rationales, and real-world examples.
Why Coherence Matters: The Missing Piece in AI Coding
Before the principles, a quick recap on coherence. As we discussed in Part 3, software thrives on a “continuous narrative” — a mental model where interfaces align, abstractions fit, and details resonate. Humans maintain this through experience; AI, with its stateless sessions, forgets it unless explicitly designed in.
In domain-heavy zones, this gap bites hard. An AI might optimize a billing module perfectly in isolation, but without understanding its DDD bounded context or Clean Architecture layer, it could violate invariants, introducing bugs that ripple system-wide. The solution? Agents engineered for awareness — specialized roles that share architectural memory via docs, types, and workflows. This builds on Part 2’s context minimization, making AI outputs verifiable and traceable.
Now, the 12 ACA-D Principles: A blueprint for agents that respect and enhance coherence.
The 12 Principles for Architecture-Aware AI Agents
1. System Context as Foundation
Every agent task starts with explicit context: architectural principles, DDD bounded contexts, and quality priorities (e.g., scalability in a supply chain system).
Rationale: Grounds AI in the narrative, preventing local optimizations that harm global fit. In practice, feed agents a JSON summary of the system’s Clean Architecture layers to scope prompts.
2. Architecture Decision Records as Memory
Agents reference and update ADRs — docs capturing decisions, alternatives, and triggers — for persistent memory.
Rationale: ADRs act as AI’s long-term recall, answering “why this design?” to avoid drift. Example: An agent analyzing a refactor checks ADRs for past trade-offs, ensuring coherence.
3. Types as Architectural Expression
Use strong types to encode DDD invariants and Clean Architecture boundaries, making them enforceable.
Rationale: Types are executable docs that guide agents, reducing hallucinations. In a healthcare app, types like PatientAggregate prevent invalid states across agent-generated code.
4. Interface-First Design Discipline
Agents design interfaces before implementation, respecting separation of concerns.
Rationale: Mirrors Part 3’s hierarchy, letting humans validate abstractions first. An agent might draft an IOrderService interface, then implement it—keeping tasks modular.
5. Domain-Driven Context Boundaries
Agents operate within DDD bounded contexts, understanding aggregates and integration patterns.
Rationale: Ensures domain alignment, as in Part 3. For a fintech system, one agent handles “Payments” context, integrating via events without bleeding into “Accounts.”
6. Hierarchical Documentation Structure
Organize docs strategically (why), tactically (what), and operationally (how), with agents accessing task-appropriate levels.
Rationale: Matches human cognition from Part 2, minimizing context. Agents pull “why” for design, “how” for code gen.
7. Progressive Refinement Workflow
Agents follow stages: requirements → architecture → interfaces → implementation → integration.
Rationale: Prevents abstraction mixing, building on staged collaboration. In a logistics tool, refine from high-level DDD models to low-level DI injections.
8. Impact Analysis Before Implementation
Agents assess changes: affected modules, architectural implications, and quality impacts.
Rationale: Forces system thinking. Before updating a module, an agent reports “This alters 2 bounded contexts; performance may drop 10%.”
9. Architectural Consistency Validation
Validate outputs against principles, patterns, and constraints automatically.
Rationale: Catches drift early. Integrate with tools like linters that flag Clean Architecture violations in agent code.
10. Living Architecture Documentation
Agents sync docs with code, maintaining “living” artifacts.
Rationale: Avoids doc-code gaps from Part 2. After implementation, an agent updates ADRs and READMEs.
11. Multi-Agent Architectural Roles
Deploy specialized agents: Architecture Analysts for impact, Interface Designers for contracts, Implementation Specialists for code, and Integration Coordinators for assembly.
Rationale: Mirrors team dynamics, sharing a coherence model. In a multi-agent setup, they collaborate via shared memory, amplifying Part 3’s layers.
12. Human-AI Architectural Partnership
Humans own the narrative; agents amplify it, evolving as tools mature.
Rationale: Balances ethics and innovation, as in Part 1. Humans review agent outputs, providing feedback loops for refinement.
Conclusion: Toward Coherent AI-Powered Engineering
These principles transform AI from a code generator into an architecture-aware partner, bridging the coherence crisis. By embedding Clean Architecture, DDD, and context strategies from prior articles, we unlock domain-heavy wins without losing the system’s soul.
Next up: Case studies of these principles in action, I’m building a tool using this idea to implement this idea, please stay tuned. Have you built multi-agent systems? Share your coherence hacks in the comments — which principle resonates most? Let’s architect the future together!