Intent-Driven Engineering: Building Software From Intent, Not Just Prompts
Intent-Driven Engineering: Building Software From Intent, Not Just Prompts
Software development is changing faster than most organizations realize.
For years, we built software by translating business requirements into specifications, specifications into tickets, tickets into code, and code into production.
Generative AI accelerated pieces of that process.
But it did not fundamentally change the process.
Intent-Driven Engineering does.
Intent-Driven Engineering starts with what we are trying to accomplish and allows AI-assisted engineering systems to reason about how to accomplish it.
Instead of giving an AI coding assistant a collection of disconnected prompts, we give it something far more valuable:
Intent.
What Is Intent-Driven Engineering?
Intent-Driven Engineering is an approach to software engineering in which developers, architects, product teams, and AI systems work from a clearly defined statement of desired outcomes.
A useful Intent describes four fundamental things:
Intent — What are we trying to accomplish?
Inputs — What information, systems, repositories, APIs, requirements, or constraints are available?
Outputs — What should be produced?
Success Criteria — How will we know the work is complete and correct?
From there, modern AI systems can help discover the existing environment, create a plan, analyze repositories, generate or modify code, test the implementation, validate results, and prepare the work for delivery.
The engineer remains responsible for the outcome.
But the engineer no longer has to manually perform every intermediate step.
That is an important distinction.
This Is Bigger Than AI Coding
Much of today’s conversation is focused on whether Claude, GitHub Copilot, Cursor, Gemini, OpenAI, or another AI tool can write code.
Of course they can.
That is becoming the least interesting part of the problem.
The more important question is:
Can we create an engineering system capable of understanding what we want, understanding the environment in which it must operate, and then progressively working toward a validated result?
That is where Intent-Driven Engineering becomes powerful.
A mature Intent-Driven Engineering workflow can include:
Repository discovery
Architecture analysis
Requirements
APIs and interfaces
Existing source code
Testing
Code coverage
Security requirements
Observability
CI/CD pipelines
Skills and reusable automation
Agents and specialized workers
MCP and enterprise data sources
Validation
Stop conditions
The objective is not simply faster code generation.
The objective is better engineering outcomes with dramatically less friction between an idea and working software.
From Prompting to Engineering
There is a major difference between saying:
“Build me an application.”
and saying:
“Here is the business problem, the environment, the available inputs, the constraints, the expected outputs, and the success criteria. Analyze what already exists, determine the smallest sensible solution, implement it, test it, and tell me when the intent has been satisfied.”
The second approach is not really prompting anymore.
It is engineering.
AI becomes part of the engineering system rather than simply a chatbot sitting next to a developer.
Progressive Intent
One of the practical ways I have been experimenting with this approach is something I call Progressive Intent.
You do not necessarily need a massive specification or complicated multi-agent architecture to begin.
Start with one Intent.
Let the AI system examine the repository and the problem.
Allow it to ask the questions necessary to reduce ambiguity.
Build the smallest meaningful increment.
Validate it.
Then continue through another progressive cycle if additional work is needed.
The loop becomes:
Intent → Context → Plan → Build → Validate → Learn → Refine
Eventually, the system reaches a point where the success criteria have been satisfied.
Then it should stop.
That last part matters.
Good AI engineering systems need to understand not only how to continue, but also when the job is finished.
The Repository Becomes the Center of AI Engineering
I believe the repository is becoming one of the most important control points in enterprise AI engineering.
The repository already contains enormous amounts of context:
Source code.
Architecture.
Configuration.
Tests.
Dependencies.
Documentation.
Deployment pipelines.
History.
Increasingly, it can also contain Intent files, reusable AI skills, validation rules, agent instructions, automation hooks, and engineering knowledge.
That means every repository can gradually become more understandable to both humans and AI.
Instead of treating AI as something outside the development process, we can make intelligence part of the engineering environment itself.
Intent-Driven Engineering Is Vendor Neutral
Intent-Driven Engineering is not dependent on one AI vendor.
The implementation may use:
GitHub Copilot.
Claude.
Cursor.
Gemini.
OpenAI.
Or technologies that have not even been released yet.
Those tools will continue changing.
The architectural principle is more durable:
Intent → Context → Reasoning → Action → Validation
That is the layer worth learning.
Why This Matters Now
The capability of AI models is increasing rapidly.
As the models improve, the bottleneck increasingly moves away from raw model intelligence.
The bottleneck becomes our ability to clearly describe:
What we want.
What the AI is allowed to use.
What constraints matter.
What success looks like.
And when the work should stop.
In other words:
The better AI becomes, the more important intent becomes.
Learn, Teach, Master
This work also fits directly into the philosophy behind Learn Teach Master.
First, we learn how these systems work.
Then we teach what we have learned.
Eventually, through repeated application, experimentation, failure, and refinement, we begin to master the underlying principles.
That journey is happening right now across the software industry.
I am documenting what I learn as I continue experimenting with Intent-Driven Engineering in real repositories, enterprise environments, prototypes, training sessions, and working software.
Watch Intent-Driven Engineering on YouTube
I am also beginning to tell this story through video.
The Intent-Driven Engineering YouTube channel is where I’ll share practical perspectives on AI, software engineering, architecture, Progressive Intent, repositories, careers, and where I believe this industry is heading.
Some videos will be technical.
Some will be strategic.
Some will challenge conventional thinking about software development and AI.
And some will simply explore where I think this industry is going.
September 13, 2026
Watch the video:
For Wix, I recommend embedding this video directly in the article and also leaving the link underneath it.
The articles let me go deeper.
The videos let me talk through these ideas more directly.
Together, IntentDrivenEngineering.com, LearnTeachMaster.org, and the Intent-Driven Engineering YouTube channel are becoming one connected body of work.
The Goal
I am not trying to predict every AI tool that will win.
That would probably be impossible.
I am much more interested in the engineering principles underneath them.
How do we turn ideas into working systems?
How do we give AI enough context without overwhelming it?
How do we create reusable engineering intelligence?
How do we maintain governance?
How do we validate what AI produces?
How do we allow engineers to operate at a dramatically higher level of abstraction?
And how do we know when the work is complete?
Those are the questions behind Intent-Driven Engineering.
And we are still very early.
Follow the Work
Visit IntentDrivenEngineering.com for the evolving framework, examples, and engineering concepts.
Visit LearnTeachMaster.org for articles about AI, software engineering, architecture, careers, and the changing technology industry.
And visit the Intent-Driven Engineering YouTube channel for the growing video series.
Learn it. Teach it. Master it.
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Intent-Driven Engineering: Building Software From Intent, Not Just Prompts
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Learn how Intent-Driven Engineering moves software development beyond AI prompting by combining intent, context, planning, automation, validation, repository intelligence, and Progressive Intent.
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Intent-Driven Engineering
Build From Intent, Not Just Prompts
For Wix, I’d publish this essentially as-is and embed the September 13 YouTube video in the Watch Intent-Driven Engineering on YouTube section.
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