Intent-Driven Engineering: It’s Not the Tool—It’s the Method
- Mark Kendall
- 7 hours ago
- 3 min read
Intent-Driven Engineering: It’s Not the Tool—It’s the Method
Intro
There’s a lot of noise right now around AI tools—new editors, new copilots, new agents. Teams are asking:
“Which tool should we use?”
But after working through real enterprise constraints, one truth becomes clear:
👉 It’s not the tool that makes you effective—it’s the method you follow.
That method is Intent-Driven Engineering.
What Is Intent-Driven Engineering?
Intent-Driven Engineering is a simple but powerful model:
Intent → Context → Execution → Validation
Intent → What you want to build
Context → How your system already works
Execution → AI (or developer) generates code
Validation → Ensure it meets standards
The Big Misconception
Most teams believe:
“If we just use the best AI tool, everything will work.”
That’s not how this works.
You can use:
…and still get inconsistent, messy results.
Why?
❌ Because the tool doesn’t know your system
✅ Unless you provide intent and context
How the Same Model Works Across All Tools
No matter what you use, the pattern stays the same.
1. Intent (The Starting Point)
This can be:
A formal intent file
A comment block in your code
A prompt
Example:
// Intent: Generate TMF697 service
// Requirements:
// - Follow existing TMF patterns
// - Enforce middleware validation
// - Use canonical model
// - Route through adapters
👉 Intent is not a file—it’s the instruction.
2. Context (The Real Differentiator)
This is where most teams fail.
Context means:
Existing patterns (TMF621, TMF632)
Middleware structure
Logging and error handling
Without context:
AI generates something valid
But not something yours
3. Execution (The Tool Layer)
This is where tools differ:
Claude Code / Cursor
Strong repo awareness
Less manual setup
Copilot
Needs files open
Works from visible patterns
Codex / ChatGPT
Requires manual context injection
👉 But all of them execute the same thing:
Your intent + your context
4. Validation (Where Enterprise Wins)
This is the step most people skip.
Validation ensures:
Middleware is enforced
Structure is consistent
No logic leaks into controllers
Adapters are clean
In enterprise environments, this is often enforced by:
CI/CD pipelines
Code reviews
Architecture standards
Four Ways to Practice Intent-Driven Engineering
You don’t need one “perfect” tool. You need a consistent approach.
🥇
Level 4 — Full Intent System
Intent files
Automated context injection
Agent-driven workflows
🥈
Level 3 — Repo-Aware AI
(Claude Code / Cursor)
Prompt = intent
Repo = context
AI does most of the work
🥉
Level 2 — Assisted Development
(Copilot + Pattern Pack)
Intent via comments
Context via open files
Developer guides generation
🪨
Level 1 — Manual Coding
No AI
Fully manual implementation
Real-World Example: TMF Architecture
In a consistent system:
TMF → Middleware → Service → Adapter → Downstream Systems
Service = orchestration
Adapter = execution
No matter the tool, this structure should not change.
The Key Insight
👉 Tools change. Patterns should not.
Why This Matters
If you rely on tools:
Your system becomes inconsistent
Every developer writes differently
If you rely on intent:
Your system becomes predictable
AI becomes an accelerator—not a risk
Key Takeaways
Intent-Driven Engineering works with any tool
Context matters more than capability
AI does not replace architecture
Consistency comes from patterns—not prompts
Final Thought
👉 “No matter the tool, if you start with intent, inject context, and validate the output—you are practicing Intent-Driven Engineering.”
If your team gets this right:
You don’t just generate code faster…
You build systems that scale consistently—no matter who or what is writing the code.
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