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Learn how Spec Driven Development transforms Agile delivery with clearer specs, AI-assisted planning, and faster, higher-quality software outcomes
For years, modern software teams have followed a familiar pattern. Business Analysts and Product teams work with stakeholders, gather requirements, prioritise features, and break everything down into user stories with acceptance criteria. Those stories are then brought into elaboration or refinement sessions with engineering, where everyone discusses the requirements, estimates them, and commits them to a two-week sprint cycle. Developers pick up the tickets, build the features, raise PRs, and the cycle repeats.
This is the traditional Agile development workflow most of us grew up with. It works reasonably well, especially in organisations with stable systems and experienced teams. But it also has well-known friction points. Context gets lost between conversations. Requirements get mistranslated into code. The original product intent slowly dilutes as engineers navigate legacy code, undocumented decisions, and assumptions that were never explicitly captured. And when teams scale up or new projects begin, onboarding becomes painful because so much context exists only in people’s heads.
Over the last 18 months, Artificial Intelligence has quietly upended this model. Not by replacing Agile, but by transforming how we design, plan, and execute the work inside an Agile organisation. A new discipline is emerging — one we’ve adopted and scaled at Ausbiz Consulting — called Spec Driven Development.
And I’ll be very direct about this: Spec Driven Development is not corporate red tape. When done properly, it unlocks enormous velocity, clarity, and quality, especially for teams building large applications or maintaining long-lived code bases. It also brings product and engineering closer than ever, and dramatically increases what you can delegate to AI safely.
This article is a long-form guide to what Spec Driven Development actually is, how it differs from traditional Agile processes, where artefacts like PRDs and agents.md fit in, and how AI is making this approach not only possible but necessary.
Agile has always emphasised collaboration, flexibility, and delivering value quickly. But as systems grow more complex, the gaps in the traditional approach become more obvious. In a typical workflow:
This works, but it has serious limitations:
And most critically: none of this workflow was originally designed with the expectation that code could be generated or reviewed by LLMs. Modern engineering teams now work with tools like GitHub Copilot, Claude Sonnet, and VS Code AI integrations — but the underlying process is still built for a pre-AI world.
Spec Driven Development is what happens when you modernise Agile for an AI-first engineering organisation.
Spec Driven Development is an AI-supported approach where high-quality specifications become the central source of truth for the entire development cycle — from requirements and design to planning, coding, testing, and documentation. The spec is explicit, detailed, and comprehensive enough that both humans and AI agents can understand it, implement it, and validate it.
In practise, Spec Driven Development relies on a few important foundations:
If traditional Agile is story-driven and human-translated, Spec Driven Development is context-driven, system-aware, and AI-augmented.
The reason this approach is emerging now is simple: we now have multiple categories of LLMs, each excelling at different parts of the SDLC.
In Spec Driven Development, you let each model do what it does best. The PRD creates the intent. The agents.md provides the context. The AI generates questions automatically to eliminate ambiguity. And from that point, the plan and code are generated with surprising reliability.
This completely changes the role of a spec. It becomes a living blueprint, a shared language between product, engineering, and AI agents.
A PRD (Product Requirements Document) is not a Word document collecting dust on a corporate intranet. It is the primary expression of business intent.
In Spec Driven Development, the PRD acts as:
The PRD becomes train-of-thought fuel for AI planning models. It allows the system to generate incredibly precise architectural plans, code scaffolding, and aligned test suites. When well-written, a PRD significantly reduces downstream ambiguity — the single biggest driver of rework in software development.
At Ausbiz Consulting, we’ve trained product managers (many from non-technical backgrounds) to write PRDs directly inside GitHub, create feature branches, and use Copilot to iterate. It has been a game-changer. Giving product teams direct access to the code and context is no longer optional — it’s now one of the fastest paths to reliable development in an AI-augmented organisation.
If the PRD captures what we want, the agents.md captures how we work.
An agents.md file typically includes:
Think of it as a developer handbook written in a format that humans and AI can both understand.
Together, the PRD and agents.md allow AI to write code that fits cleanly into an existing ecosystem instead of generating something that looks correct but violates the subtle rules of your code base.
And one emerging trend we're exploring is external context storage — letting multiple repositories access shared architectural knowledge through an external MCP-connected store. This opens the door to organisation-wide consistency rather than repo-by-repo guesswork.
Traditionally, planning is something humans do manually, and often inconsistently. With Spec Driven Development, AI plays a central role in the planning workflow:
A well-written spec doesn’t only describe the feature. It becomes the foundation for:
Pro-tip: Always include a section prompting the AI to ask clarifying questions and explicitly document assumptions. This eliminates ambiguity early and dramatically improves final code quality.
And yes — modern models can now run long-horizon plans that execute over the course of an entire day, from scaffolding to implementation to testing. But those long-horizon workflows only succeed when the spec and context are strong.
Vibe Coding is the term I use to describe how many developers — especially in fast-moving environments — prefer to work: hands on keyboard, building prototypes quickly, iterating based on intuition and taste, and letting the design evolve as they go.
There’s nothing inherently wrong with Vibe Coding. For small experiments or personal side projects, it’s fantastic.
But for serious systems, especially enterprise-scale ones with long-lived code bases, complex domains, and multiple engineering teams, Vibe Coding becomes a liability. It leads to architectural drift, inconsistent quality, and massive onboarding overhead.
Spec Driven Development feels slower upfront, but the “time debt” works in your favour. You invest in clarity early, and you get enormous dividends during implementation, testing, and maintenance.
A lot of vibe coders initially see this as corporate bureaucracy. But the truth is: once you bring AI into the mix, the cost of poor context compounds rapidly, because AI magnifies ambiguity. If you want to safely scale AI-augmented engineering, good specs are non-negotiable.
We adopted this approach across multiple teams, and a few changes made the biggest impact:
The result: faster delivery, dramatically better code quality, fewer misunderstandings, and a measurable drop in rework.
Agile isn’t going away. If anything, Spec Driven Development strengthens Agile by reducing overhead, increasing alignment, and enabling AI-augmented engineering teams to operate at a much higher velocity.
The future belongs to teams who:
The organisations that master Spec Driven Development will out-ship their competitors, onboard faster, and scale more predictably.
And the ones that keep relying purely on vibe, intuition, and tribal knowledge will struggle as their systems and teams grow.
This is not a small optimisation. It is a foundational shift in how modern software is built.