Over the past few months I built an AI-assisted delivery framework — not to write code faster, but to eliminate ambiguity across the entire software development lifecycle.
The result completely changed how I think about AI in engineering.
The problem I kept hitting
Every time I used AI to generate architecture docs, API contracts, or implementation plans across separate sessions, the outputs looked great in isolation. But viewed together? They were broken. A pivot in the system architecture was never reflected in the API contracts. Frontend assumptions silently diverged from backend data models.
AI wasn't the problem. Treating it as a collection of disconnected prompt sessions was.
What I built instead
A governance-driven framework built on three layers:
Prompt → Agent → Skill
- The Prompt captures intent only — lightweight, declarative
- The Agent orchestrates execution and decides which capabilities to invoke
- The Skill is a reusable, schema-validated execution block with hardcoded governance rules
This connects every delivery artifact into a sequential dependency chain:
Business Requirements
↓
System Architecture
↓
Data Architecture
↓
Event Architecture
↓
API Contracts
↓
Implementation Plans
↓
Backend / Frontend Implementation
Each artifact consumes the one before it. Upstream changes automatically propagate downstream. Governance is enforced at the Skill layer — not buried in fragile prompts.
The finding that surprised me most
The highest-leverage use of AI wasn't code generation.
It was context generation.
When engineers — or downstream agentic workflows — were given a governed, unambiguous spec, implementation quality was consistently higher than any raw AI-generated code output. The context was the unlock, not the syntax.
What failed
I'm including this because most write-ups skip it:
- Over-orchestrating everything (not every workflow needs an agent loop)
- Prompt bloat as a substitute for real architecture
- Severely underestimating token costs at scale
- Believing full pipeline autonomy was a safe goal — it isn't
Full write-up
I covered the complete framework, the frontend design extraction layer, backend implementation with a real IAM module, the honest retrospective, and where this goes next in a detailed Medium article:
👉 AI-Driven SDLC: Beyond Code Generation to Delivery Orchestration
Would genuinely love to hear if others have run into the artifact drift problem and how you've handled it. Has anyone built something similar?