Intent-Driven Engineering with Claude

Intent-Driven Engineering with Claude

A Structured, Repeatable Framework for Taking Ideas to Production-Ready Code

Modern engineering teams are no longer measured solely by how well they write code.

They are measured by how quickly they can transform ideas into structured, governed, production-ready solutions.

This class introduces a practical, repeatable framework for doing exactly that — using AI as a reasoning engine inside the developer workflow.

The objective is simple:

Take an idea from the back of a napkin and convert it into structured, constraint-aware, governed code within one working session.

If this can be done consistently for one team, it can be scaled across many.

The Model: Three Planes of AI Maturity

The class is structured around three progressive planes of capability.

Plane 1 – Personal Mastery

Intent-Driven Engineering

Engineers learn how to:

• Translate ambiguous requirements into structured intent

• Define constraints before writing code

• Provide contextual prompts that drive deterministic outputs

• Use AI as a reasoning partner, not an autocomplete tool

This phase focuses on discipline and clarity.

AI amplifies structure — it does not replace it.

Plane 2 – Team Integration

Claude Embedded in Daily Workflow

Using:

• Visual Studio Code

• Claude

Engineers learn how to:

• Integrate Claude directly into the development environment

• Reason across multiple files

• Refactor code to align with constraints

• Audit implementation against governance rules

• Improve architectural consistency in real time

The focus here is operational:

AI becomes part of the team’s structured engineering process.

Plane 3 – Future State

Human + AI Orchestration

This introduces forward-looking concepts:

• Multi-agent workflows

• Automated governance checks

• Drift detection

• Pipeline-integrated enforcement

• Cross-team orchestration models

This phase defines where the organization can evolve next.

The Core Demonstration: Napkin → Governed Code

The class includes a live working demonstration that follows a clear progression:

1️⃣ The Napkin

A lightweight business requirement is introduced:

“Build a simple API that receives product orders, validates pricing rules, and returns approval or rejection.”

No architecture yet. No code.

Just business intent.

2️⃣ Structured Intent

The requirement is formalized into:

• business-intent.md

• constraints.md

• governance.md

This introduces engineering discipline before implementation begins.

Intent defines purpose.

Constraints define boundaries.

Governance defines accountability.

3️⃣ Scaffold Generation

Claude is prompted to generate:

• Project structure

• API scaffold

• Basic models

• Logging framework

The team observes how structured intent produces structured output.

4️⃣ Constraint-Aware Refactoring

Claude is instructed to refactor the scaffold according to:

• Clean architecture rules

• Configuration-driven design

• Separation of concerns

• Logging standards

This demonstrates reasoning across files.

5️⃣ Governance Audit

Claude is prompted to:

• Audit the repository against governance rules

• Identify violations

• Suggest corrections

This proves that AI can:

• Understand policy

• Evaluate structure

• Recommend alignment

All inside the developer workflow.

What This Class Proves

This is not a coding exercise.

It demonstrates that structured teams can:

• Reduce ambiguity early

• Translate business requirements into executable artifacts

• Apply architecture principles consistently

• Reason across codebases in real time

• Improve solution velocity from day one

The transformation is visible:

Napkin → Intent

Intent → Constraints

Constraints → Scaffold

Scaffold → Governance Audit

Audit → Refactor

That progression is repeatable.

Technical Setup (Minimal Friction)

The environment is intentionally simple:

• macOS workstation

• Visual Studio Code

• Claude

• Git repository with structured intent files

No complex pipelines.

No orchestration engines.

No heavy configuration.

The goal is clarity, not complexity.

Outcomes for Engineering Teams

By the end of the session, participants will understand:

• How to formalize intent before implementation

• How to use AI to reason over entire repositories

• How to apply governance rules dynamically

• How structured prompts outperform generic prompting

• How AI can enhance — not replace — disciplined engineering

Most importantly, they will see that:

AI is most powerful when paired with structured teams.

Scalability

The framework is intentionally designed to scale.

If one structured team can consistently transform requirements into governed code using this method:

• The same model can be rolled out across multiple teams

• Governance patterns can be standardized

• Intent templates can be shared

• Constraint libraries can be reused

• Orchestration layers can be introduced incrementally

This is not a one-off demo.

It is a capability model.

Closing Principle

Engineering excellence does not disappear in the age of AI.

It becomes more important.

AI amplifies clarity.

AI amplifies structure.

AI amplifies disciplined teams.

This class demonstrates how to harness that amplification deliberately and repeatably.