Intent-Driven Development: A Modern SDLC for AI-Accelerated Teams

The traditional SDLC wasn’t designed for AI-accelerated delivery. When features ship in hours instead of weeks, detailed upfront documentation becomes the bottleneck in an AI-assisted software development lifecycle.

As organizations accelerate delivery with AI, most software documentation strategies haven’t kept pace. Practices built for slower, predictable releases struggle with rapid iteration.

Modern software delivery teams need a lighter approach that emphasizes outcomes over upfront prediction and replaces heavy specs with build-first documentation, capturing decisions in real time and documenting what actually ships.

Intent-Driven Development is an evolution of agile and modern SDLC practices. It aligns on intent upfront, builds iteratively to learn and refine, and documents shipped reality through build-first documentation.

The result: faster feedback, stronger ownership, and documentation teams can actually trust. Here’s how the approach works in practice.

What You’ll Learn About Modern AI-Assisted Software Delivery

By the end of this article, you’ll have a practical framework to modernize your AI-assisted SDLC. Specifically, you’ll discover:

• Why traditional requirements and documentation fail in AI-accelerated environments
• Intent-Driven Development principles for faster delivery
• How build-first documentation keeps teams aligned
• A three-phase model you can adopt immediately
• When this works best (and when to stick with traditional approaches)

Core Concepts: Intent-Driven Development and Build-First Documentation

• Intent-Driven Development: an SDLC approach where product and engineering align on intent (problems, outcomes, boundaries), then build to learn and refine.
• Build-First Documentation: a modern software documentation strategy that captures decisions during development and formalizes documentation after delivery, ensuring accuracy and alignment with the system that actually ships.

The Core Problem with AI-Assisted Development Workflows

With AI-assisted development tools like Claude Code, the development phase compresses dramatically while other phases remain the same.

Traditional Flow

With AI-Assisted Development

The bottleneck has shifted from development to requirements and testing.

The Philosophy Behind Intent-Driven Development

Intent-Driven Development is built on a simple reality: in fast-moving environments, the code becomes the most reliable source of truth.

What you learn while building often changes the solution itself. Trying to fully specify every detail up front slows momentum and creates documentation that drifts almost immediately.

Intent-Driven Development isn’t about writing less documentation, it’s about changing when and why we write it. Instead of trying to predict the full solution up front, teams align on intent first:

• • the problem to solve
  • the outcomes that matter
  • the boundaries to respect

Then they build to learn.

Teams use build-first documentation to capture decisions during development and formalize documentation after delivery, so artifacts reflect what actually shipped rather than what was imagined. The result: documentation that stays accurate without slowing delivery.

Mindset Shift:

Traditional Mindset: “Document what we plan to build, then build it to spec”
Intent-Driven Mindset: “Capture intent, build to learn, document what we actually built”

Spec-First vs Intent-Driven Development: Choosing the Right SDLC Approach

This isn’t a rejection of spec-first development – it’s a different tool for a different kind of work.

Platforms like Amazon Web Services and Microsoft often promote spec-first development, which works well when requirements are stable or interfaces must be precise (APIs, highly regulated systems, integrations).

But in AI-accelerated or discovery-heavy work, requirements evolve during the build. Upfront specs quickly age, and documentation becomes the bottleneck. That’s where Intent-Driven Development and build-first documentation fit better.

Most mature teams use both, depending on context. We do.

The Three Phases of Intent-Driven Development

Phase 1: Intent Capture (Before Development)

In Intent-Driven Development, Phase 1 replaces heavy, prescriptive user stories with lightweight intent capture. Instead of writing detailed requirements and acceptance criteria, the team aligns on the intent (the “why”), outcomes, and boundaries for the feature.

Traditional User Story (Problematic)

Title: Agent Email Queue


As a claims agent
I want to see inbound customer emails in a queue
So that I can respond to inquiries efficiently


Acceptance Criteria:
- Queue displays sender name, subject, timestamp
- Queue sorted by received date descending
- Agent can filter by status...
- ... (20+ more detailed criteria)

Build-First Intent Document (Better)

Title: Agent Email Queue


Intent:
Claims agents need a centralized place to see and respond to
customer email replies. Currently checking multiple Outlook inboxes.


Success Looks Like:
- Agents can see all inbound replies in one place
- Agents can claim ownership of an inquiry
- No customer emails fall through the cracks


Boundaries:
- MVP: Basic queue with assign/resolve workflow
- Not yet: Advanced routing, SLA tracking

Key Differences in Phase 1

• No implementation details (sorting, filtering specifics)
• Clear success criteria without prescribing solutions
• Explicit scope boundaries
• Unanswered questions listed (resolved during build)

This keeps the focus on business intent while leaving room for discovery during development.

Phase 2: Build & Discover (During Development)

Phase 2 is where Intent-Driven Development and build-first documentation come together.

Developers don’t jump straight into coding. Instead, they start each cycle by creating a lightweight implementation plan that outlines how they intend to achieve the feature’s intent. This plan provides structure and progress visibility throughout the day, while remaining flexible enough to adapt as discovery happens.

Developers then build iteratively, making small decisions along the way and capturing them in real time through Daily Ship Notes. Daily Ship Notes are the core build-first documentation artifact during development. These notes create visibility during development and become the raw material for final documentation.

Below is the same feature shown at two points in time: first the implementation plan, then the Ship Notes captured after delivery.

How Phase 2 Works

Plan

• Sketch a simple implementation approach for the day or sprint
• Break the intent into small, testable increments
• Identify risks, assumptions, or open questions

Build

• Implement in small increments aligned to the intent
• Adjust scope as new information emerges
• Raise questions immediately to product and stakeholders
• Treat code and tests as living documentation of system behavior

Capture

• Record decisions and tradeoffs in Daily Ship Notes
• Summarize what actually shipped and why

The implementation plan is forward-looking (what we expect to build). Ship Notes are backward-looking (what we actually built).

Example – Lightweight Implementation Plan (Start of Day)

## Agent Inbox – Today’s Plan


Goal: Deliver MVP queue with assign/resolve workflow


Steps:
- Create queue API endpoint
- Persist messages to DB
- Add basic UI list view
- Implement assign/resolve actions
- Add simple priority flag
- Validate with product before adding filters


Risks / Questions:
- Expected SLA behavior?
- Do we need real-time updates or polling?


Done when:
- Agents can see, claim, and resolve emails end-to-end

Example – Daily Ship Notes (End of Day)

(In practice, these are generated from commit messages and augmented by developer notes in tools like Jira or Teams.)

## Agent Inbox - Build Notes


### Nov 28 - Started
- Set up basic queue endpoint
- Decision: Using SignalR instead of polling (better UX)


### Nov 29 - Core workflow
- Added assign/resolve workflow
- Decision: Added priority field - urgent items need visibility
- Question for product: What should default SLA be?

These lightweight notes preserve context and decisions without slowing delivery. They become the build-first documentation that rolls directly into the final post-build documentation.

Phase 3: Document the Delivery (After Development)

Phase 3 formalizes build-first documentation into durable artifacts. Here, teams create post-build documentation that reflects reality, not assumptions. Because it is based on what shipped, this documentation is both accurate and trustworthy.

Post-Build Documentation typically includes

• Overview of what was built
• Capabilities delivered with brief descriptions
• Screenshots and demo videos where applicable
• Key decisions made during build (with rationale from Ship Notes)
• Known limitations and tradeoffs
• Future considerations or next iterations

Because this documentation describes the system that actually exists, it stays useful over time and supports audits, onboarding, and QA.

Common Questions About Intent-Driven Development (Estimation, QA, Compliance)

Teams adopting Intent-Driven Development often raise concerns about estimation, QA processes, compliance requirements, and cross-team alignment. Below are the most common questions enterprise teams ask when implementing build-first documentation in an AI-assisted software development lifecycle.

How do we estimate without detailed requirements?

Intent-Driven Development shifts estimation from task-level decomposition to outcome-level sizing. Instead of estimating individual filters, searches, or UI elements, teams estimate capabilities at the intent level. For example, an “Agent Inbox MVP” may be estimated as a 3–5 day effort based on scope and boundaries rather than granular sub-tasks.

This capability-based estimation works well in AI-accelerated development environments, where build speed is compressed and requirements evolve during implementation.

How does QA know what to test?

In a build-first documentation model, QA validates the delivered capability against the original Intent Document. Post-Build Documentation, demo videos, and Daily Ship Notes provide a clear record of what was actually implemented.

This shifts QA from validating pre-written acceptance criteria to validating shipped reality, improving alignment between product, engineering, and quality teams.

How does Intent-Driven Development support compliance and audit requirements?

We’ve used this approach on modernization projects in regulated industries, including insurance and financial services. In those environments, documentation and traceability are required. Intent-Driven Development doesn’t reduce documentation. It changes when it’s written and how it’s maintained.

Instead of relying on detailed specs that often drift during development, we maintain a clear artifact trail throughout the lifecycle:

• Intent Documents that define requirements and scope boundaries
• Daily Ship Notes that capture implementation decisions as they happen
• Post-Build Documentation that reflects what was actually delivered
• Just-in-Time Jira stories written after delivery to align formal acceptance criteria with shipped functionality

Because documentation is based on what was built (not what was originally predicted), it tends to be more accurate during audits and easier for new team members to understand.

We’ve found that this structure satisfies compliance expectations while still allowing teams to move quickly.

What if the developer builds the wrong thing?

Intent-Driven Development reduces this risk through tight feedback loops. Daily Ship Notes provide visibility into implementation decisions in near real time.

If a deviation from intent occurs, product teams can course-correct within hours rather than weeks, preventing misalignment from compounding.

How do we handle dependencies between teams?

Intent-Driven Development applies to feature implementation, not contract definition. API contracts and cross-team interface agreements are still defined upfront within the Intent Document.

This preserves architectural integrity while allowing implementation flexibility within defined boundaries.

Build-First Documentation Artifacts and Deliverables

This stack supports Intent-Driven Development across all three phases:

Before Development

• Intent Document: Why are we building this? What does success look like? What’s in/out of scope?
• Napkin Sketch (optional): Rough UI concept, key workflows

During Development

• Daily Ship Notes: Daily async updates with developer’s running log of decisions, questions, and scope adjustments
• Code + Tests: The code itself serves as living documentation of system behavior

After Development

• Post-Build Document: What was actually built, how it works, screenshots, decisions log
• Just-in-Time Story: Jira/compliance story created in Done status with links
• Release Notes: User-facing summary (if needed); can be repurposed from Post-Build Document to be more audience targeted

This artifact stack ensures transparency, auditability, and accuracy while minimizing documentation waste in AI-accelerated SDLCs.

When to Use Build-First Documentation (and When Spec-First Works Better)

Build-first documentation works best in modern software delivery environments where teams move quickly, learn through iteration, and have the autonomy to make decisions. Like any SDLC approach, it isn’t one-size-fits-all.

Ideal For:

• Internal product teams with trusted senior engineers
• Discovery-heavy features where requirements evolve
• AI-accelerated development where build cycles are compressed
• Teams prioritizing rapid feedback and iteration

Use with Guardrails

• Regulated industries (insurance, healthcare, financial services)
  • Maintain intent documents and post-build artifacts for audit trails
  • Add formal sign-off after delivery
  • Define API and interface contracts up front

Not ideal For:

• Fixed-scope or outsourced contracts that require detailed upfront specifications
• Multi-team initiatives with strict interface dependencies
• Safety-critical systems where behavior must be fully defined before implementation

The goal isn’t to eliminate documentation. It’s to create it at the moment it’s most accurate and useful.

Traditional vs Build-First Documentation: Side-by-Side Comparison

Real-World Results: Modernizing Delivery with AI and Build-First Documentation

At Keyhole, we use Intent-Driven Development on modernization and AI-accelerated delivery engagements where traditional specs can’t keep pace.

The impact is measurable. Teams consistently compress delivery timelines from months to weeks while still meeting enterprise standards for visibility, auditability, and compliance. In one recent engagement, we reduced an estimated 18–24 month effort to approximately five months by aligning on intent, building iteratively using AI, and documenting what actually shipped.

Our teams using variations of this model have reported:

• 30–50% less time spent writing upfront specifications
• Faster feedback cycles between product and engineering
• Earlier edge cases and risk discovery
• Higher developer satisfaction and ownership
• Development goes from idea->implementation in hours making true agile iteration possible

Because decisions are captured throughout the build and formalized after delivery, documentation doubles as a practical knowledge base. New team members ramp faster, stakeholders stay aligned, and ownership transfers cleanly from Keyhole senior consultants to client teams long after the engagement ends.

How to Get Started With Intent-Driven Development and Build-First Documentation

Here’s a practical 5-step plan to implement Intent-Driven Development and build-first documentation on your next feature:

• Pick your next feature that hasn’t been detailed yet
• Write an Intent Document (30 minutes max) capturing why, success criteria, and boundaries
• Build an implementation plan to accomplish the success criteria
• Iterate as decisions/challenges/blockers come up during the process
• Post Daily Ship Notes to Teams/Jira during development
• Compile Post-Build Documentation after delivery (screenshots, decisions, capabilities delivered)
• Create Just-in-Time Jira Story in Done status with links to all artifacts

Run this for two features, then retrospect. Our senior consulting teams have consistently found more accurate documentation, happier developers, and better product visibility into what’s actually shipping.

This low-risk pilot proves the approach works for your AI-accelerated workflows.

Summary: A Modern Documentation Strategy for AI-Accelerated Teams

Intent-Driven Development modernizes your SDLC for AI-accelerated teams by making build-first documentation practical. Instead of spending days writing detailed specifications that become outdated the moment development starts, you:

• Capture intent quickly (problem, outcomes, boundaries)
• Build with developer autonomy (developers solve the problem)
• Maintain transparency (Daily Ship Notes keep everyone informed)
• Document shipped reality (what was actually built, via Post-Build Documents and Just-in-Time Stories)

This approach is particularly effective for teams using AI-assisted development tools where the build phase has compressed dramatically. The documentation stays accurate because it’s written after the fact, and the tight feedback loop of daily Ship Notes catches misalignments before they become costly.

This delivers 30-50% less spec-writing time, exponentially faster delivery times for code, faster feedback, and accurate artifacts proven across Keyhole Software’s modernization engagements. Start with two features to see the difference in speed, alignment, and developer satisfaction – and you will end with eyes-wide realizations that what used to be fantasy is now very much reality!