The Socratic Method of Software Development

For two decades, the software industry has operated under a single orthodoxy: agile is the way. Waterfall — with its rigid phases, upfront planning, and sequential execution — was declared dead, a relic of an era when software was expensive to produce and impossible to iterate on quickly. Agile promised a better world of short sprints, continuous feedback, and rapid pivots.

Then artificial intelligence fundamentally changed the economics of software development, and the industry discovered something uncomfortable: the methodology it had discarded may have been ahead of its time — it was simply waiting for the right collaborator.

That collaborator has arrived. Not as a tool that writes code faster, but as an intelligence capable of doing what no previous software tool could: asking the right questions before a single line of code is written. The result is a new methodology — one that borrows the rigour of waterfall, the adaptability of agile, and adds something neither ever had: a Socratic engine that drives specification quality to levels that were previously impractical to achieve.

The Specification Crisis

The story of most failed software projects is not a story of bad engineering. It is a story of bad specifications. Requirements that were incomplete, contradictory, or simply wrong. Assumptions that went unchallenged. Edge cases that no one thought to ask about. The Standish Group's CHAOS reports have repeated this finding for decades: the number one cause of project failure is incomplete or changing requirements.

Agile's answer was elegant in theory: don't try to get the specification right upfront. Instead, build a little, show it to the customer, get feedback, adjust, and repeat. This worked when code was expensive to write and cheap to change. But the rise of AI coding agents has inverted that equation entirely. Code generation is now nearly free. Regenerating an entire module from scratch takes minutes, not months. What remains expensive — arguably more expensive than ever — is knowing what to build.

"Speed without direction is just a faster way to reach the wrong place."— César Soto-Valero, on the limits of vibe coding

The "vibe coding" movement of 2025 demonstrated this painfully. Developers and non-developers alike began conversationally prompting AI to generate applications on the fly — no spec, no architecture, no plan. The results were fast and often impressive in demos. They were also, overwhelmingly, unmaintainable. Studies showed that 66% of developers using AI tools spent more time fixing generated code than they saved writing it. The problem was never the AI's coding ability. The problem was that nobody had asked the right questions first.

Enter the Socratic Phase

The Socratic method — named after the Greek philosopher who taught not by lecturing but by asking probing questions — turns out to be a remarkably effective model for how AI should interact with the people who understand the problem domain. Instead of treating AI as a code generator that takes orders, the Socratic approach treats it as an intelligent interviewer: one that systematically questions the solutions architect, the product owner, and the domain expert to surface the complete picture of what needs to be built.

In practice, this looks like a structured dialogue. The product owner describes the desired system. The AI — in most production implementations today, Anthropic's Claude — begins probing. Not superficial clarification questions, but deep, systematic interrogation of the kind a senior consultant would conduct over weeks, compressed into hours.

"What happens when a user initiates a transaction but loses connectivity mid-process?" "You've described the happy path for invoice approval — what happens when the approver is on leave and the invoice is overdue?" "Your authentication requirement specifies SSO, but you haven't addressed session timeout behaviour for users with multiple active tabs. What's the expected behaviour?" These are the kinds of questions that typically surface only during development — when the code is half-written and the answer requires expensive rework. The Socratic phase surfaces them before a single function is defined.

But the AI does more than ask questions. It actively challenges assumptions. When a stakeholder says "this will only ever be used by internal employees," the AI pushes: "Your roadmap mentions a partner portal in Q3. Should the access model anticipate external users now, or is a migration acceptable?" When a product owner specifies a data model, the AI stress-tests it against the stated reporting requirements and flags inconsistencies. This is not passive requirements gathering. It is adversarial specification refinement — the intellectual equivalent of a red team for your product requirements.

The output of this phase is not a conversation log. It is a formal specification document — structured, unambiguous, and comprehensive — that becomes the single source of truth for everything that follows. It captures functional requirements, non-functional requirements, edge cases, data models, integration points, error handling strategies, and acceptance criteria. It is, in effect, a contract between the stakeholder's intent and the system that will be built.

From Specification to Software: The Waterfall Build

Once the specification is locked, the methodology shifts into what is unmistakably a waterfall execution model — and deliberately so. The spec feeds into a coordinated pipeline of AI coding agents, each handling a defined portion of the system. Supervisor agents orchestrate the work, ensuring that the output of one agent is compatible with the inputs expected by another. Testing agents validate each component against the acceptance criteria defined in the specification. Documentation agents generate technical and user documentation in parallel.

This is where the timeline compression becomes dramatic. A traditional six-month waterfall project spends roughly 20% of its time on requirements, 15% on design, 40% on implementation, 15% on testing, and 10% on deployment and documentation. The Socratic method compresses the requirements phase by an order of magnitude — not by skipping rigour, but by conducting it at machine speed with human expertise. The implementation and testing phases compress even further, because the specification is detailed enough for AI agents to execute against with minimal ambiguity.

The data emerging from early adopters supports this. AWS teams using the Kiro IDE reported cutting a two-week feature build to two days by front-loading specification work. One startup tripled its development productivity by adopting exhaustive specs before handing implementation to AI agents. Anthropic's own research found that Claude generates substantially more robust applications when provided with detailed, structured specifications compared to iterative conversational prompting.

The Pivot to Agile: Post-Release Iteration

Here is where the Socratic method diverges from pure waterfall — and where it becomes genuinely powerful. Traditional waterfall ends at deployment. Whatever was built is what the customer gets, and any misalignment between the delivered system and the customer's actual needs becomes the subject of a painful change request process. This rigidity is, rightfully, the primary criticism of waterfall.

The Socratic method treats the first release as a hypothesis, not a final product. The specification was thorough — far more thorough than any agile backlog or traditional requirements document — but no specification is perfect. Users will interact with the system in ways that were not anticipated. Business conditions will shift. New integrations will become necessary.

At this point, the methodology pivots explicitly to agile. Short iteration cycles — measured in days, not weeks — allow rapid adjustment based on real user feedback. The difference from traditional agile is that these iterations are refining and extending a system that is already architecturally sound and functionally complete, rather than incrementally building toward an unclear target. This is not iteration born of uncertainty. It is iteration born of precision — the fine-tuning of a system that was built correctly the first time.

The practical effect is that post-release agile cycles in the Socratic method are dramatically shorter and more focused than traditional sprints. There is no architectural debt to work around. There are no half-built features to complete. The iterations address genuine user feedback, not the backlog of work that should have been in the original scope.

Why the Economics Have Changed

The Socratic method works because the fundamental economics of software development have inverted. For forty years, the bottleneck was implementation — writing code, debugging code, refactoring code. Every methodology optimised for this constraint. Agile minimised wasted implementation effort by building incrementally. Waterfall tried to reduce rework by planning thoroughly upfront. Both were, at their core, strategies for managing the scarcity of developer time.

AI has removed that constraint. Implementation is no longer the bottleneck. When an AI coding agent can generate a complete module in minutes, and regenerating it from scratch is cheaper than patching it, the calculus changes fundamentally. The new bottleneck is specification clarity. The organisations that invest in getting the specification right — truly, comprehensively right — are the ones capturing the full productivity gains of AI. The organisations that skip this step are the ones in the 66% who report spending more time fixing AI-generated code than they save.

"The SDLC was designed for an era when code was expensive to produce. That era ended. The new constraint is specification clarity and evaluation quality. Optimise for that, and AI becomes the 95% performer that research promised."— Leverage AI, "Waterfall Per Increment"

This is why the Socratic phase is not optional overhead — it is the highest-leverage activity in the entire development cycle. Every hour spent in rigorous, AI-assisted specification gathering saves days or weeks in the build phase. Every edge case surfaced before development begins is an architectural decision made correctly the first time, rather than a costly refactor discovered in production.

The Evidence Is Mounting

The shift toward specification-first, AI-executed development is not theoretical. Anthropic's 2026 Agentic Coding Trends Report documents that 57% of organisations now deploy multi-step agent workflows, and that engineers are increasingly shifting from writing code to coordinating agents that write code. TELUS reported that its engineering teams shipped code 30% faster while creating over 13,000 custom AI solutions. Rakuten achieved 99.9% accuracy on modifications to a 12.5-million-line codebase in seven autonomous hours.

The tooling ecosystem is responding accordingly. AWS's Kiro IDE is built entirely around a spec-to-design-to-tasks-to-implementation flow. JetBrains VP Arun Gupta has stated publicly that spec-driven development will become mainstream in 2026, describing a future where development splits cleanly into thinking (humans write specifications) and doing (agents build accordingly). The frameworks emerging to support this — BMAD Method, GSD, Ralph Loop, GitHub Spec Kit — all share the same core insight: the quality of the specification determines the quality of the output.

What the Socratic method adds to this emerging consensus is the recognition that specification quality is not merely a documentation problem. It is an interrogation problem. The best specifications do not come from product owners writing requirements in isolation. They come from structured, adversarial dialogue — the kind that a well-trained AI is uniquely positioned to conduct at scale, with patience, and without the social dynamics that cause human teams to avoid hard questions.

The Road Ahead

The Socratic method of software development is not a rejection of agile. It is a recognition that agile was optimised for a constraint that no longer binds. When implementation was expensive, iterating on incomplete specifications made sense. When implementation is nearly free, iterating on incomplete specifications is waste — and the organisations that eliminate that waste will outperform those that do not, by an order of magnitude.

The evidence from early adopters is clear: front-loading specification quality through rigorous, AI-driven dialogue, executing against that specification with coordinated AI agents, and reserving agile iteration for post-release refinement produces results that neither methodology achieved alone. Projects that once took six months are completing in six weeks. The specification is better, the code is more consistent, and the customer gets a working product sooner.

The future of software development begins not with a prompt, but with a question.