From Vibe Coding to Spec-Driven Development

Written By: Matt Elgin, Senior Staff Consultant at Callibrity

#RoadtoTechFest

Adding Specification Context to Your AI-Enabled Development Process

Vibe coding is wreaking havoc by introducing instability to a traditionally disciplined development process. The ability for anyone to prompt an AI and receive working code within seconds has fundamentally changed how developers approach implementation. It feels fast, intuitive, and, at times, almost frictionless. It is also introducing the same failure patterns that have existed in software delivery for decades, only at a higher velocity.

When development begins without clear goals, grounded functional definitions, or a well-articulated implementation plan, the result is not innovation. It is rework. AI does not eliminate the consequences of poor planning. It amplifies them. The absence of specification leads to misalignment, and misalignment at scale leads to costly failures in implementation and ongoing maintenance.

This is the transition point organizations are now facing. Moving from what can be described as “vibe coding” to a more structured specification-driven approach that introduces clarity, context, and repeatability into AI-enabled development workflows can improve outcomes for any organization.

Why Spec-Driven Development Matters More with AI

What seems to be missing is not capability, but structure and clarity in the process. Spec-driven development is not a new concept, but its importance has increased significantly in the context of AI-assisted delivery. At its core, it is the discipline of defining what needs to be built with enough clarity that execution can proceed with minimal ambiguity.

Traditional Agile practices rely on product requirement documents and user stories with acceptance criteria to define an approach, but these artifacts are not optimized for agentic processes. Product requirements are very difficult to capture. Once translated into user stories, these stories are intentionally lightweight and designed to facilitate discussion. That model works when experienced engineers interpret intent and fill in gaps with advice from the product experts. Vibe coding and direct agentic prompting isn’t able to fill in the gaps in the same way.

This isn't a problem the industry has missed. GitHub's Spec Kit, Amazon's Kiro, and the patterns emerging around Claude Code are converging on the same conclusion: agents need machine-readable specification, not interpretive prose. What's missing in most enterprises isn't the tooling but the organizational discipline to treat specification as a first-class artifact rather than a meeting byproduct.

Without a structured specification that can be passed into the agentic system, AI coding agents generate output based on incomplete or inferred context. The result may be syntactically correct, but functionally misaligned to the intent of the overarching requirement. The issue doesn’t reside in the capability of the coding agent, it is input quality. When specification becomes the starting point, the development process shifts from reactive implementation to intentional execution. The outcome is not just faster delivery, but more accurate delivery.

In one recent modernization engagement, an experienced team generated more working code in a single sprint than they had in the previous quarter. They discarded nearly half of it the following sprint, not because the code was broken, but because it failed to honor constraints the agent had no way of knowing existed. Nobody had written them down to provide the developer utilizing the agent to ensure the specification actually drove the implementation.

Introducing the Scribe

Historically, scribes played a critical role in preserving and translating knowledge, societal rules and religious tenets. They interpreted existing manuscripts, adapted them for new audiences, and ensured that meaning was retained even as context evolved. They provided the expertise across languages and methodologies, along with the conceptual grounding that would allow them to translate information into new artifacts with precision.

In modern AI-enabled development, a similar role is emerging that would align to this purpose. In modern terms, the “scribe” becomes the individual or function responsible for translating business intent and technical constraints into structured specifications that both humans and AI systems can execute against. These tasks are not limited to a single person with all the responsibility. It spans product owners, technical leads, and senior engineers who understand both the problem space and the system architecture. In practice, it does require a single individual to drive the process to transcribe the output of these requirements into usable artifacts. This role is typically a senior technical lead with strong product instincts who can own the process and outcomes, rather than a rotating responsibility shared across the team. Diffuse ownership produces diffuse specifications which leads to the implementation of misaligned requirements.

This includes articulating functional requirements, identifying dependencies, establishing constraints, and ensuring that the specification reflects both the desired outcome and the realities of the system it will operate within. The quality of this work directly determines the effectiveness of downstream execution.

In AI-enabled delivery, the scribe is not optional. It is the control point that determines whether AI accelerates outcomes or amplifies ambiguity.

Scribes in Practice

In practice, spec-driven development introduces a structured workflow that precedes code generation. The process begins with iterative alignment. Rather than issuing a single prompt, the scribe engages in a back-and-forth dialogue with business and technical stakeholders and directly with AI models to refine context. Outcomes are clarified, as assumptions are verified and constraints are made explicit.

Only once there is shared understanding does the implementation phase begin.

This separation between planning and execution is critical. It ensures that the development phase is grounded in a well-defined specification rather than exploratory prompting. Once development is complete, the process does not end. Human review remains essential. Reviewing code diffs ensures that implementation aligns with intent. A secondary review done by another engineer or an AI system in review mode introduces an additional layer of validation.

The process is simplified to the following steps:

Define the domain and project specification | Align with product, architecture and subject matter experts to establish the rules and constraints for the project
Define the requirements and generate business and technical specification artifacts | Product owners come up with the use cases and technical leads can plan out the detailed requirements using AI agents in plan mode performing the scribe coding process. These artifacts and plans can be attached to the epics and user stories to drive development
Drive implementation from the specifications | The development team utilizes the detailed plan specifications as a starting point for coding agent execution, guiding the coding agent through each of the use cases
Perform a multi-layered review with the agents and the scribe working together | Put the agent into review mode and have it review the coding implementation. Work with the agent to iterate and improve the code and ensure testing is performed successfully. Then perform the traditional PR review utilizing the development team to ensure the end result matches the specifications.

This structured loop, which defines the specification, execution, and review process, creates consistency that vibe prompting alone cannot achieve. It creates the artifacts necessary to capture the critical ideas that are crucial to not only the implementation but to the ongoing maintenance and support of business critical systems.

Persistent Specifications

One of the more significant advantages of spec-driven development is that specifications become durable assets.

Unlike transient prompts or isolated user stories, specifications can live alongside the codebase, fed directly into tooling that can develop and maintain the specifications. They provide ongoing context for future development, enabling developers and coding agents to understand not just what the system does, but why it was built that way. As features evolve and requirements change, these specifications can be updated to reflect new understanding to drive the iterative development process. This creates a persistent specification in which historical context is preserved while the system continues to adapt and evolve.

For organizations managing complex systems or legacy modernization efforts, this is particularly valuable. It reduces dependency on tribal knowledge and ensures that future contributors, human or AI, have access to the context needed to make informed decisions.

This is where the transition from vibe coding to scribe coding delivers the most significant return.

Prescribed Conclusions

Vibe coding optimizes for speed and improvisation. Scribe coding utilizes vibe tactics enhanced with traditional development practices to generate the specification which leads to a more accurate implementation and maintainable solution. For AI-enabled development processes, the distinction matters.

The organizations that succeed will not be the ones generating the most code. They will be the ones defining the problem space with enough precision that both humans and machines can execute effectively to solve the problem and iterate on the solution as it evolves into a mature product.

In AI-enabled systems, what you define becomes what is built. Specification is no longer external documentation. The specification is built directly into the system.

If you are working through any of this or just want to compare notes with people who spend most of their time in exactly this space, let's talk. Callibrity will be at TechFest in Durham on May 13.

Learn more at callibrity.com