AI can generate code. Harnesses can validate behavior. But who builds understanding?
This is Part 1 of the AI-Native Software Engineering series.
The series asks a larger question:
When AI lowers the cost of implementation,
what remains scarce in software engineering?
This article starts with the first scarce resource:
Understanding.
Over the past few months, I’ve been experimenting heavily with AI-assisted software development.
Not autocomplete.
Not AI as a coding copilot.
But a more agentic workflow:
SPEC
-> Constraints
-> Harness
-> Agent Execution
-> Evaluation
-> Iteration
The experience has been fascinating.
I was able to ship projects using languages and frameworks I was not deeply familiar with. Development speed increased dramatically.
For small projects, this felt like magic.
For larger projects, something unexpected happened.
I discovered that implementation velocity and system understanding are not the same thing.
And eventually I hit an uncomfortable realization:
If the AI disappeared tomorrow, I might struggle to continue developing parts of my own system.
That realization changed how I think about software engineering.
The Traditional Model: Understanding Through Implementation Link to heading
Historically, software engineering looked something like this:
Human
-> Write Code
-> Build Mental Model
-> Operate System
Writing code was not only an implementation activity.
It was also a learning mechanism.
You learned by:
- debugging
- refactoring
- tracing execution
- fighting constraints
- making mistakes
Implementation created understanding.
Even if we didn’t realize it.
Agentic Coding Changes the Direction Link to heading
With AI coding, the loop changes.
Human
-> Describe Goal
AI
-> Generate Implementation
Human
-> Review Output
This is powerful.
But something subtle happens.
The implementation layer becomes compressed.
You see results.
You validate behavior.
But your brain never fully constructs the internal model.
You start operating systems that feel increasingly like inherited codebases.
Not because the code is bad.
But because your understanding was outsourced.
Harness Engineering Solves Correctness, Not Understanding Link to heading
My response to this problem was to introduce constraints.
Instead of letting AI freely generate software:
SPEC
-> Feature Gates
-> Harness
-> Implementation
-> Evaluation
This worked surprisingly well.
Harnesses externalize correctness.
Instead of relying on:
I believe the implementation is correct.
You move toward:
The system behavior satisfies explicit constraints.
Examples:
Input
-> Expected Output
Load Test
-> Expected Latency
Failure
-> Expected Recovery
Feature
-> Acceptance Criteria
This dramatically improves reliability.
But after using this workflow extensively, I realized something important:
Harnesses prove behavior.
Harnesses do not create understanding.
The Problem Is Not Losing Coding Ability Link to heading
Initially I thought:
Am I becoming dependent on AI?
Now I think that was the wrong question.
The real question is:
How do humans build mental models when implementation is delegated?
Because modern AI can:
- modify 20 files
- introduce abstractions
- restructure modules
- migrate frameworks
much faster than humans can absorb.
The bottleneck is no longer writing.
The bottleneck becomes understanding.
A New Skill: Collaborative Deep Diving Link to heading
I no longer believe the answer is:
Humans must understand every line.
That does not scale.
Instead:
Humans need to remain capable of descending into implementation when needed.
Think of it as collaborative deep diving.
Normal operation:
Behavior
-> Architecture
-> Constraints
Incident mode:
Failure
-> Runtime Trace
-> Code Path
-> Implementation
-> Root Cause
AI becomes the exploration engine.
Humans maintain the cognitive loop.
Not:
Human -> Code
But:
Human
-> Question
AI
-> Expand Context
Human
-> Build Model
AI
-> Inspect Details
Human
-> Judge
Designing Systems for Diveability Link to heading
This changed how I think about engineering.
I care less about whether I can manually write everything.
I care more about whether I can re-enter understanding at any layer.
For every feature, I now want:
Why
Why does this exist?
Invariant
What must never break?
Flow
How does data move?
Risk
Where does failure happen?
Debug Entry
Where should I start?
Not documentation.
Not code comments.
Understanding interfaces.
Closing Thoughts Link to heading
AI lowers the cost of implementation.
That does not eliminate engineering.
It shifts the bottleneck.
From:
Can you build?
To:
Can you understand what was built?
Harnesses validate.
Agents execute.
But mental models still need to be constructed.
And increasingly, that construction may become a collaborative process between humans and AI.
Not less engineering.
A different kind of engineering.