How Feature Teams Should Use Intent-Driven Engineering to Move Faster

How Feature Teams Should Use Intent-Driven Engineering to Move Faster

Most software teams do not fail because they lack tools.

They fail because the work is not clear enough when it reaches the developer.

That is becoming more obvious now that teams are using AI coding tools like Claude Code, GitHub Copilot, Cursor, and others. These tools can move very fast, but they cannot magically solve ambiguity. If the feature is unclear, the output will be unclear. If the business rules are missing, the code will guess. If the story is too large, the developer and the AI assistant will both get pulled into the entire system instead of the feature in front of them.

This is where intent-driven engineering becomes practical.

Not theoretical. Not philosophical. Practical.

It gives feature teams a way to turn epics, stories, designs, screenshots, business rules, APIs, data flows, and acceptance criteria into a clear feature-level work order that both the developer and the AI assistant can execute against.

The Problem: Teams Are Asking Developers to Build From Too Much Ambiguity

A common pattern is starting to show up inside companies using AI coding tools.

They have epics.

They have stories.

They have Figma screens.

They have repositories.

They have some intent files.

They have developers using Claude Code.

But when the work gets to the feature branch, the developer still does not have enough clarity.

The developer may know the story title. They may have a Figma screen. They may have a few acceptance criteria. They may know which repo they are working in. But they still have to figure out:

What exactly is this feature supposed to do?

Which screen owns the behavior?

Which API should be called?

What are the required fields?

What validations are needed?

What business rules are hidden behind the screen?

What database objects are involved?

What happens after the user clicks submit?

What error states need to be handled?

What is out of scope?

What proof is needed in the pull request?

That is too much discovery happening during development.

AI does not remove the need for clarity. It increases the value of clarity.

Figma Is Useful, But Figma Is Not the Feature

Figma can be a great starting point.

A screen design gives the team something concrete. It shows the layout, fields, buttons, drop-downs, modals, tabs, labels, and basic user experience. For UI-heavy applications, especially data-driven onboarding or operational applications, a Figma screen can help Claude Code understand what needs to be built.

But Figma does not usually contain the full feature.

A screenshot does not explain all the business rules.

A modal does not tell you what happens after submit.

A text box does not tell you validation logic.

A drop-down does not tell you where the values come from.

A button does not tell you which API to call.

A screen does not explain downstream fulfillment, workflow status, audit rules, security, or error handling.

So the right move is not “build from Figma.”

The right move is:

Use Figma to help create the feature intent. Then build from the feature intent.

That is the difference.

The Missing Layer: Feature Branch Intent

Most teams already understand epics and stories.

The epic describes the broader business outcome.

The story describes a user need.

But when a developer opens a feature branch, they need something more specific.

They need a feature branch intent.

A feature branch intent is the tactical execution file for that feature. It is not a giant architecture document. It is not a business requirements document. It is not a random AI prompt.

It is a focused work order that says:

Here is the feature.

Here is the screen.

Here is the behavior.

Here are the fields.

Here are the business rules.

Here are the APIs.

Here is the validation.

Here are the files or areas likely involved.

Here is what is in scope.

Here is what is out of scope.

Here is how we will know it is done.

That is what Claude Code needs.

That is what the developer needs.

That is what the reviewer needs.

That is what the feature team needs.

The Right Flow for Feature Teams

A strong feature team should not jump straight from story to coding.

The better flow looks like this:

Epic → Story → Figma/design reference → Feature intent → Claude Code implementation → PR evidence → Dev deployment

This creates a clean operating model.

The epic gives business direction.

The story gives user value.

Figma gives the visual target.

The feature intent gives implementation clarity.

Claude Code accelerates the build.

The pull request proves the work.

DevOps moves the feature forward with evidence.

That is how teams start turning AI coding into real delivery acceleration.

Keep the Feature at the Story Level

One mistake teams make is letting a “feature” become too large.

A feature should not mean “everything related to onboarding.”

It should not mean “finish the whole customer workflow.”

It should not mean “build the screen, the API, the database, the import process, the fulfillment process, and the reporting changes all at once.”

That is not a feature. That is a mini-project.

For AI-assisted delivery, the feature should stay close to the story level.

A good feature might be:

Build the Add Contact modal.

Add validation to the onboarding screen.

Wire the Save button to an existing API.

Display imported data in a review table.

Add status update behavior for one workflow step.

Create an error state for failed imports.

Add one field group to an existing screen.

Those are feature-sized.

They can be understood.

They can be implemented.

They can be reviewed.

They can be tested.

They can potentially fit into a two-day feature delivery model.

But only if they are prepared correctly.

The Two-Day Feature Goal Is Possible, But Not by Wishing

Many organizations want a simple rule:

One feature. One developer. One day to build. One day to review and promote.

That sounds great.

But it only works when the feature is small enough and clear enough.

A two-day feature goal is not impossible. But it requires discipline before development starts.

The team has to stop using the developer’s first day as discovery time.

If the developer spends half the day figuring out the business rule, finding the right API, asking where the data comes from, checking which repo owns the change, and trying to understand what the Figma screen means, then the two-day target is already broken.

To make the two-day model work, the team needs a Day 0.

Day 0 is not a long planning ceremony.

Day 0 is intent readiness.

Before the feature branch starts, the feature intent should already be clear enough for a developer and Claude Code to execute.

Day 0: Intent Readiness

Before coding starts, the team should create the feature intent.

This can be done quickly using the story, Figma screen, repo context, API patterns, and known business rules.

The feature intent should answer the basic execution questions:

What is the user trying to do?

What screen or component is being changed?

What fields are involved?

What business rules apply?

What data is being created, updated, imported, or displayed?

What API endpoint is needed?

What validation is required?

What error and success states are expected?

What tests should be added?

What is excluded from this feature?

What evidence must be included in the PR?

This is where the speed comes from.

Not from rushing the developer.

Not from skipping validation.

Not from forcing every feature into one day.

Speed comes from reducing ambiguity before the developer starts.

Day 1: Build With Claude Code

Once the feature intent is ready, the developer can use Claude Code much more effectively.

Instead of saying, “Build this feature,” the developer gives Claude a scoped intent file.

Claude can then work against clear instructions:

Build this modal.

Use this existing component pattern.

Follow this API convention.

Apply these validation rules.

Use this schema.

Handle these error states.

Add these tests.

Do not change unrelated parts of the system.

This is how AI coding becomes controlled acceleration instead of uncontrolled generation.

The developer is still responsible for engineering judgment. Claude Code is not the owner. The developer is.

But the developer now has a high-quality assistant working from a clear feature-level contract.

Day 2: PR, Review, Evidence, and Dev Promotion

The pull request should not just contain code.

It should contain evidence.

The PR should show:

What feature intent was used.

What files changed.

What screen or modal was implemented.

What API was called.

What tests were added or updated.

What validation was included.

What screenshots prove the feature works.

What assumptions were made.

What is not included.

This changes the PR review.

The reviewer is no longer just reading code and trying to reverse-engineer intent.

The reviewer compares the implementation against the feature intent.

That is faster.

That is cleaner.

That is safer.

And for DevOps, QA, and product owners, it creates a visible trail of what was actually delivered.

A Practical Feature Intent Template

A feature branch intent does not need to be complicated.

It can be simple and repeatable:

Feature Name

What are we building?

Story/Epic Reference

Which story or epic does this support?

User Outcome

What should the user be able to do when this is complete?

Figma/Design Reference

What screen, modal, or design is the source?

Scope

What is included in this feature?

Out of Scope

What should not be changed?

Fields and UI Behavior

What fields, buttons, drop-downs, tabs, modals, or tables are involved?

Business Rules

What rules govern this feature?

Validation

What must be required, formatted, blocked, calculated, or checked?

API/Data Contract

What endpoint, payload, response, and error handling are expected?

Repository/Code Areas

Which repo, project, component, service, or module is likely involved?

Testing Expectations

What unit, component, API, or integration tests are needed?

Definition of Done

How will the team know this is complete?

PR Evidence

What screenshots, test results, and notes must be included?

This is enough to turn vague work into executable work.

Why This Matters Across Multiple Repos

When a company has seven repos, feature clarity becomes even more important.

A developer should not have to rediscover the whole system for every story.

The feature intent should help identify:

Which repo owns the UI.

Which repo owns the API.

Which repo owns shared models.

Which repo owns background processing.

Which repo owns infrastructure or deployment changes.

Which repo should not be touched.

Without that clarity, AI coding tools can create unnecessary changes across too many files or too many repos.

Intent-driven engineering keeps the feature bounded.

That is critical when teams are trying to move fast.

The Real Lesson

The lesson is not that every feature can magically be completed in two days.

The lesson is that many features can move much faster when they are sliced correctly and prepared properly.

The phrase feature teams should remember is this:

A feature can only move fast after it has been made clear enough to move fast.

That is the heart of intent-driven engineering.

It does not replace Agile.

It does not replace developers.

It does not replace product owners.

It does not replace design.

It connects them.

It takes the epic, the story, the Figma screen, the business rules, the APIs, the data model, the tests, and the pull request evidence, and turns them into one executable feature-level intent.

That is how teams using Claude Code can finally start seeing real productivity gains.

Not just faster code.

Faster delivery.

Final Thought

If your feature team wants to hit a two-day feature delivery model, do not start by pressuring the developer to code faster.

Start by improving the quality of the feature intent.

Make the feature smaller.

Make the rules clearer.

Make the API contract visible.

Make the Figma screen useful, but not the only source of truth.

Make the PR evidence-based.

Then let Claude Code help the developer execute.

That is how intent-driven engineering moves from concept to operating model.

And that is how feature teams begin turning AI-assisted development into real delivery velocity.This one is strong because it does not oversell the “one feature, two days” claim. It keeps the ambition, but makes the operating model mature: Day 0 intent readiness, Day 1 build, Day 2 PR/evidence/dev promotion.