Vibe coding and spec-driven development (SDD) are two emerging approaches where devops teams use AI to develop all of an application’s code. There are discussions about which approach to use for different use cases, and there are many platforms to consider with varying capabilities and experiences. Some experts question whether AI delivers reliable, maintainable applications, while others suggest that, at some point, AI can lead the end-to-end software development process.
But one certainty IT organizations face is that there’s more demand for applications, integrations, and analytics than there is supply of agile teams and devops engineers. Compound this imbalance with business priorities to address application security vulnerabilities, modernize applications for the cloud, and address technical debt. It results in tough choices on what work to prioritize and where to drive efficiencies in the software development life cycle.
Even before AI code generators emerged, IT leaders sought ways to improve developer productivity. Platforms like 4GL, low-code/no-code, and configurable SaaS helped IT deliver more applications, reduce the developer skill set required to release enhancements, and improve software quality. These tools enabled IT to develop entire classes of applications, analytics, and integrations that couldn’t be built easily or cheaply by coding in Java, .NET, and other programming languages.
“Software has long been treated like infrastructure: built to last, hard to change, and expensive to replace, says Chris Willis, chief design officer and futurist at Domo. “That model is giving way to a future with more applications that are smaller, faster to build, and created to solve a specific job before getting out of the way.”
Code gen, vibe, or write a spec?
GenAI models are the next accelerators for software development. The first tools were copilots for coding assistance, followed by LLMs for generating code snippets. I used code-generation tools to develop regular expressions, extract information from web pages, and categorize data as steps in an app migration. They wrote code that I no longer had the time or skills to develop on my own, but it still required significant work to fix defects and integration issues.
We’re now in a second-generation phase of AI software development, with platforms like Amazon Q Developer, Appian AI-Assisted Development, Bolt, Claude Code, Cline, Cursor, Gemini Code Assist, GitHub Copilot, Kiro, Lovable, OpenAI Codex, Pave, and Replit.
All these platforms generate code, but they offer different developer experiences and are used to address different scopes of work. They can be broken down into three categories:
- Code-generating tools enhance the developer experience by writing code on request from engineers and are often integrated into existing development tools.
- Vibe coding generates prototypes, features, and production-ready applications through an iterative prompt-based experience.
- Spec-driven development (SDD) creates an intermediary step before generating applications by allowing a development team to establish product requirements and compose other design documents iteratively through prompts, then generating code from them.
If you are developing a new API, refactoring existing code, enhancing a workflow, or building a new feature, then a code generator may be all you need. The developer’s work shifts from writing code to expressing what code needs to be written, the requirements, the development platform, and other non-functional acceptance criteria.
But what if you want to develop a new application, integration, data pipeline, or a robust web service? For this article, I wanted to look beyond code generation and consider how development teams can use vibe coding and spec-driven development platforms to build and support applications.
What vibe coding does well
The vibe coding experience enables developers to prompt what they are looking to build and to observe the AI as it generates code.
Vibe coding platforms like Bolt, Lovable, and Replit can start developing from a single prompt, but they demonstrate more capabilities when the developer goes into plan mode. In planning, a vibe coding platform may repeat back the requirements it understands, ask questions to elaborate on them, and offer options when requirements aren’t specified.
The “vibe” you get from these platforms is that they want to help developers go from idea to a functioning application quickly. Developers can then prompt the platform to refine requirements and request changes. And it’s not just developers; business owners, non-technical startup founders, and other citizen developers are vibe coding, though they must learn the security best practices.
“Vibe coding enables groups within the organization to create minimal viable products or small-scale tools that greatly increase their productivity,” says Duncan Ng, vice president of solutions engineering at Vultr. “Examples span proofs of concept that you want to put in front of potential consumers to receive feedback on product market fit, to laborious processes that can be streamlined to generate efficiency gains and increase velocity.”
Are vibes a viable production path?
A proof of concept (POC) or minimal viable product may be all a developer needs, but some question whether vibe-coded applications are ready for production. Rajesh Padmakumaran, vice president and AI practice leader at Genpact, says, “Vibe coding accelerates POCs, rapid experimentation, and idea exploration, but it lacks deterministic behavior, making it fundamentally unsuitable for systems that need to be maintained, scaled, or supported long-term.”
The negative sentiment isn’t just targeted at vibe coding, but at AI-generated code in general. Low-code and no-code platforms faced similar concerns in their early years around security, architecture, performance, and operational resiliency. Successful platform vendors established trust through transparency, and IT departments learned what scaffolding, processes, and documentation were needed to scale low- and no-code development. A similar transition is likely to happen with vibe coding platforms.
“Vibe coding accelerates experimentation, but without clear architectural constraints, observability, and performance guardrails, it introduces variability that breaks downstream systems in devops and IT operations,” says Piyush Patel, chief ecosystem officer at Algolia. “CIOs should treat vibe coding as a front-end accelerator while anchoring systems in well-defined specs that act as the ‘prompt layer’ for both humans and AI.”
Start with requirements
Another approach for using AI to develop applications is spec-driven development. Rather than jumping right into prompts to steer AI’s application development, SDD platforms shift-left the process, helping engineers document requirements. Based on those requirements, the SDD platforms then develop the application.
“Spec-driven development is all about structure and accountability,” said David Yanacek, senior principal engineer of agentic AI at AWS. “You spend some time talking about what you want and what good looks like, and it responds with requirements, a technical design, and a breakdown of the development tasks.”
Yanacek is an advisor to AWS Kiro’s development team. Much like non-AI development projects start with designs, product requirement documents, and agile user stories, SDD reinforces the need for collaborating across business and technology stakeholders before jumping into code. Two successful use cases are a drug-discovery AI agent deployed to production in three weeks and a technology company’s accelerated cloud migrations.
“Creating these documents keeps the AI focused on high-quality output, so I can go back and verify that it did what I asked it to,” adds Yanacek. “For example, the design document describes the system’s behavior in detail, including code snippets and the database schema. When you fully specify how a system or feature should behave, the agent can generate more and better tests to verify its output.”
SDD is gaining traction among devops teams that recognize the importance of collaborating with stakeholders on both feature and non-functional requirements.
“Spec-driven development is the natural maturation and evolution of vibe coding, where teams are fully maximizing the context window of their agent,” says Austin Spires, senior director of developer marketing at Fastly. “Spec-driven vibe coding forces engineers and teams to have a clearer vision, firmer requirements, and stronger writing than the first iterations of vibe coding.”
Nic Benders, chief technical strategist at New Relic, adds, “Production software doesn’t start with coding. It starts with thinking about the problem, figuring out what you want, and communicating that with your team. Spec-driven development puts a brand name on doing that thinking and writing, but with an AI tool as your team.”
Competing or complementary?
Are SDD and vibe coding competing approaches? Will an enterprise support two different methodologies? Or is SDD an evolution of the vibe coding experience? “Vibe coding and spec-driven development aren’t competing approaches; they’re complementary ones, each with a distinct role in the development life cycle,” says Ayaz Ahmed Khan, senior director of engineering at Cloudways by DigitalOcean. “Use vibe coding to explore and prototype, and spec-driven development with AI to harden and ship. The teams that succeed with genAI are the ones who mindfully guide it with constant feedback to build production-ready software.”
Others suggest that vibe coding and SDD will continue to serve different business needs and implementation strategies. “Vibe coding, especially with capable agentic systems, delivers extraordinary velocity for user-facing prototypes where the blast radius of a defect is small, like for internal tools or first POCs,” says Wiktor Walc, CTO at Tiugo Technologies. “But the moment you’re dealing with large production environments, distributed state, or transactional integrity, you start benefiting from spec-driven contracts between services—not because today’s models can’t reason about complex systems, but because no agentic workflow yet offers the kind of deterministic correctness guarantees that production-critical infrastructure demands.”
Focus on resilient releases
Planning and coding are just two steps in building and supporting applications. There are other opportunities to use AI in the software development life cycle for developing AI agents, including building in observability, integrating Model Context Protocol servers, and robust AI agent testing.
World-class IT departments need to consider how vibe coding and SDD drive business value, innovation, and reliability, more than just improving the coding aspects of delivering applications. To what extent does AI develop solutions that meet business requirements and deliver exceptional user experiences?
“Both vibe coding and SDD assume that the hard work of getting business and IT stakeholders aligned on the right requirements is already done, and this is especially true as enterprises look to reimagine and redesign many of their core workflows to leverage AI,” says Don Schuerman, CTO and vice president of marketing and technology strategy at Pegasystems. “The real opportunity for AI is not just to accelerate how code gets written, but to provide a collaborative canvas where business and IT teams can generate the designs and requirements for a truly reimagined application together.”
Much of today’s excitement is around how AI accelerates application development and developer productivity. But what about the deployment process and the infrastructure to run AI-developed applications?
One emerging trend is AI application development platforms that come bundled with cloud deployment infrastructure and business process automation services. AI-Assisted Development from Appian supports spec-driven development through its business interface Appian Composer and development tools such as Claude, Codex, and Kiro. Pave is a vibe coding platform that deploys to the same secure infrastructure as Quickbase and leverages its governance capabilities. These two examples illustrate how low-code development and process management platforms are evolving to embrace AI capabilities.
Experts remind IT leaders that whether you code, vibe, or adopt SDD, the emphasis should be on delivering resilient applications.
“The focus should be on engineering discipline and system design rather than pitting vibe coding and spec-driven development against each other,” says Sergei Kondratov, director of development at Saritasa. “The success of any AI-assisted development today depends on how well tasks are broken down and controlled. If that is done poorly, both approaches fail.”
Other experts point out that the quality of AI-generated code and the ease of maintaining AI-generated applications are open questions.
“Spec-driven development orients teams toward the right business and technical outcomes, while AI coding increases velocity, says Christian Stano, field CTO at Anyscale. “What matters is the interface where production software actually ships, where focus should solve the real bottleneck: whether review processes, infrastructure, and guardrails can keep pace. The key metric isn’t speed alone, but whether teams are accelerating without trading off reliability or accumulating hidden technical debt.”
Hannes Hapke, director of the 575 Lab at Dataiku, adds, “While vibe coding compresses the time to first demo, there are major concerns about debt, security, and auditability. Spec-driven preserves discipline but adds overhead, and the key opportunity is blending both. CIOs need to measure impact through time to release, bug rates, refactoring frequency, and developer satisfaction, not just velocity.”
There’s no doubt that vibe coding and SDD will evolve, and there’s a reasonable chance the two practices will converge into a generalized AI coding environment. One example is GitHub’s Spec Kit, which works with GitHub Copilot, Claude Code, and Gemini CLI, and treats spec writing as a prerequisite to vibe coding and code generation.
As AI’s development capabilities improve, IT will need to consider how to evolve the end-to-end development process and ensure new capabilities do more than improve velocity and productivity.