This month, we hosted the first AI Native Dev meetup in London, with our friends from Codurance. We had some great discussions about AI Native Development at the event, with a couple of talks, one of which was given by me. During the session, I talked about the comparison of code-centric and spec-centric software development, which resonated with the audience. I was recently asked if there was a blog post on this topic, and I was surprised I hadn’t written one yet, so here it is! Codurance one of which was given by me spec-centric software development Code as the source of truth In today’s reality, we use a code-centric approach to software development. During the creation of an application, we typically start with a requirements doc, or a PRD that describes the intent of the application. From this document, or set of documents, we write our code in our language of choice. I’m not here to judge, unless your choice is PHP. The next stage is to get some tests (ok, I can hear TDD fanatics sigh. You pick your own order!) and make sure our code is working as intended before building and packaging our code up to ship. Oh, don’t forget those docs, too. Take a look at the slide I used to talk through this, and the words used to describe each of the artefacts. The requirements are marked as disposable, as over time, they tend not to keep up with the latest version of the code. The code however, is long-lived, because it’s the source of truth. It’s what is changed and updated, and shipped. So if there's an issue in production, the code holds the answers, not the out-of-date requirements docs. The reason tends to be due to the way we evolve our codebase, by making: Enhancements - improvements to our code, adding new features that are being asked for by users and product teams.Bug fixes - Issues in our code, likely because we have missing tests, incomplete requirements, or maybe even a security issue.Edge cases - those awkward flows or cases we didn’t even think about or dream a user would try to do.Ecosystem changes - upgrades to libraries or frameworks, that could require updates to our code. Enhancements - improvements to our code, adding new features that are being asked for by users and product teams. Enhancements Bug fixes - Issues in our code, likely because we have missing tests, incomplete requirements, or maybe even a security issue. Bug fixes Edge cases - those awkward flows or cases we didn’t even think about or dream a user would try to do. Edge cases Ecosystem changes - upgrades to libraries or frameworks, that could require updates to our code. Ecosystem changes We know the reality is that these changes are made in our codebase and rarely reflected back into the requirements docs. We’ve now lost the connection between our intent (the what) and our implementation (the how). Very often, the tests and docs also start to fall out of sync, as code updates aren’t reflected with updated or new tests, and docs become stale as they become out of date without strong development processes and diligent developers. Oh, and I know you’re diligent, it’s all the others out there that aren’t. This results in a very fragile application with blurred lines between the what and the how, which now both reside in the code, which we often can only work out if we are able to talk with some of the original authors and maintainers of the code. This is, of course, subject to them still being available or a part of the org that created the code, and their ability to remember what happened. If their memory is anything like mine, good luck with that. We end up with the same old maintenance problems that we’ve unfortunately grown all too used to. How do AI assistant tools help? The typical AI developer tools that are popular today, such as GitHub Copilot, Cursor, Windsurf etc, help us create code faster, and make changes. However, they’re essentially updating the code. How about agentic tools? They pretty much all still focus on updating the code too. The code still ends up as the source of truth. We’re still code-centric and are accelerating the problem. Some parts are erased. This doesn’t really make the best use of AI in the software development process. Code Specification unleashes the true potential of AI Let’s look at what happens when we make specs the new source of truth. In order to do this, the spec must be the primary artefact that the developer changes. It’s not a document that decorates or accompanies the code. It’s the document that the code is derived from at all times. If an enhancement or bug fix comes in, the spec is updated to reflect the change. The spec should also contain the descriptions of the tests, because they also define intent. They state how the component should act, given various permutations of input and state. Using your favourite LLM, the code can now be generated from this complete spec, along with runnable tests, as well as documentation. Everything is in step and accurate to the specification. Here’s how the spec-centric approach looks, where the spec is now the long-lived artifact. What does spec-centricity unlock? The benefits are pretty impressive as this becomes autonomous. I’ll mention a few here that are discussed on the slide below: Software is less fragile: With code being generated alongside more complete tests, we can continuously validate the code with the tests to ensure the implementation we’re creating aligns with the intent we detailed in the spec.Adaptable software: Perhaps we created a node implementation initially, but since we like to make bad life choices, we now decide we want a PHP implementation. So long as we keep the specification language and ecosystem agnostic, we can supply context during code generation that specifies how we want that generation to happen, in this case we’d say we’d want to create the component in PHP alongside the tests to again make sure we can validate our intent has been implemented correctly.Autonomous software: Many issues, such as a framework update, an implementation, or 3rd third-party security issue, don’t actually require a specification change. They’re not updating intent, but rather altering how a component is created, based on security scans, etc. As a result, we unlock a path where the code could autonomously be regenerated and validated once again to the spec, to apply this maintenance.Higher quality software: The generation of the code could not just be checked for quality, but also performance, security, and other non-functional requirements. Changes to the generation could be iterated upon, but the key is always to ensure that we’re not only more secure and faster (based on the needs of how the component will be used) but still able to check that the developer's intent has been successfully tested and validated.Self-optimizing software: The context that we pass to the generation can also come from data we gather from the production environment. We can collect system telemetry data, and automatically spin up different code generations based on observations of real production usage, and continuously optimize our implementation while continuously validating every change against the spec through tests. Software is less fragile: With code being generated alongside more complete tests, we can continuously validate the code with the tests to ensure the implementation we’re creating aligns with the intent we detailed in the spec. Software is less fragile Adaptable software: Perhaps we created a node implementation initially, but since we like to make bad life choices, we now decide we want a PHP implementation. So long as we keep the specification language and ecosystem agnostic, we can supply context during code generation that specifies how we want that generation to happen, in this case we’d say we’d want to create the component in PHP alongside the tests to again make sure we can validate our intent has been implemented correctly. Adaptable software Autonomous software: Many issues, such as a framework update, an implementation, or 3rd third-party security issue, don’t actually require a specification change. They’re not updating intent, but rather altering how a component is created, based on security scans, etc. As a result, we unlock a path where the code could autonomously be regenerated and validated once again to the spec, to apply this maintenance. Autonomous software Higher quality software: The generation of the code could not just be checked for quality, but also performance, security, and other non-functional requirements. Changes to the generation could be iterated upon, but the key is always to ensure that we’re not only more secure and faster (based on the needs of how the component will be used) but still able to check that the developer's intent has been successfully tested and validated. Higher quality software: Self-optimizing software: The context that we pass to the generation can also come from data we gather from the production environment. We can collect system telemetry data, and automatically spin up different code generations based on observations of real production usage, and continuously optimize our implementation while continuously validating every change against the spec through tests. Self-optimizing software Final thoughts If this resonates with you and you can feel the value that spec-centric development can offer your software development workflows, i’d recommend watching the video either in it’s entirety, or just the bit I cover these slides. A couple of key things that stand out for me: video the bit I cover these slides Testing becomes more important than the code. Yes, we don’t ship the tests, but with more autonomous code generation, it’s how we validate what good looks like.A spec-centric workflow massively opens up opportunities of multiple implementations per spec, with accompanying context, self-optimizing software, autonomous maintenance, and more.This approach requires developers to care more about intent, as well as other skills. Patrick Debois covers this really well in The 4 patterns of AI Native Development.There are a ton of open questions that we need to answer for this to be a reality. What is a spec? How do you validate? How do you version adaptable implementations? How do you engage with LLM decisions? What is the role of an AI Native Developer? Testing becomes more important than the code. Yes, we don’t ship the tests, but with more autonomous code generation, it’s how we validate what good looks like. A spec-centric workflow massively opens up opportunities of multiple implementations per spec, with accompanying context, self-optimizing software, autonomous maintenance, and more. This approach requires developers to care more about intent, as well as other skills. Patrick Debois covers this really well in The 4 patterns of AI Native Development. Patrick Debois The 4 patterns of AI Native Development There are a ton of open questions that we need to answer for this to be a reality. What is a spec? How do you validate? How do you version adaptable implementations? How do you engage with LLM decisions? What is the role of an AI Native Developer? Join our community on Discord and subscribe to our mailing list to join our community to be a part of this movement and to contribute to answering some of these questions and many more! Discord subscribe to our mailing list

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As AI Tools Accelerate Coding, Advocates Push for Specs as Source of Truth

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