I've been watching this trend toward SDD. Makes sense but it feels like the process pendulum is swinging back toward the pre-agile era of functional specs and design documents. Not quite Big Design Up Front[0] but maybe increasingly working software == comprehensive documentation[1]?
Waterfall anyone?!
[0] https://en.wikipedia.org/wiki/Big_design_up_front
[1] https://agilemanifesto.org/
Functional specs and design documents are just programming in a natural language. In the olden days it took a human to "code" that into a programming language, but now that compilers (i.e. LLMs) are getting better at compiling natural language, it might look like you're able to skip a step (to varying degrees of success).
Whereas agile doesn't care what language you build your software in. It's about taking managers out of the picture; encouraging developers to get involved with what are normally considered "managerial" tasks. The 12 Principles goes into more detail about the things developers might need to do if there are no managers.
Spec driven development is a good idea, but the current implementations are trash because they hand off markdown files to an agent who might as well be wiping its ass with them for all the reproducibilty you get. If you're going to have agents generate specs they should be structured and transformable via code gen in to actual stub code and tests. It's only a little bit more work than unstructured markdown specs, saves a bunch of time in terms of boiler plate generation and gives you very high reproducibility.
Do you have any documents how this could be achieved? Thanks
Take a look at the CLI subproject of https://github.com/sibyllinesoft/arbiter. It does all this. I am in the process of making a version of the CLI that's standalone with a fully open license, I'm just swamped ATM getting a side hustle ready for No Kings.
Distinguished Engineer and AI-assisted delivery expert at Thoughtworks.
And then talk about memory banks. Yeah, I recognize that from work where "AI has taken off" as well.
Guess what: As memory banks grow or accumulate the AI gets confused and doesn't quite deliver.
So far, a human that actually knows their product still prevails and is necessary to actually guide any AI effort. AIs have been trying to bullshit me so much it's not even funny any longer. Of course they all apologize and figure out reality when I guide them but that doesn't change the facts. And I simply can't read all the documents the AIs write for themselves to correct all of them and even if I did I wouldn't be sure enough that they'd improve significantly enough for me to try and spend this mind bogglingly boring amount of time to help this thing that's supposed to take my job ....
What exactly are you trying to say about their role description and the talk of memory banks?
Memory is a bad idea right now, because it requires well tuned retrieval to deliver value, and one sized retrieval systems don't work, full stop. Most memory systems are designed around a homogenous chat paradigm and produce negative results in heterogenous chat environments or non-chat based agentic workflows.
The right way to do "memory" is to feed it to a "metacognition/default mode" network that builds a theory of mind / task ideation structure async from the main agent, then injects context relevant steering into the agent for each prompt based on this metamodel. So, "agentic memory" basically.
It’s on there, right? And that “thought leader” title they’ve put on LinkedIn? I’m still scratching my head trying to figure out what that means!
This pretty much aligns with my experience with SpecKit - I'm excited by it, and enjoying working with it, but have had a hard time finding guidance on advanced real world use cases.
All the tutorials I've found are little more than "here's how to install it - now let's make a todo list app from scratch!!"
Would be great to see how others are handling real world use cases like making incremental improvements or refactorings to a huge legacy code base that didn't start out as a spec driven development hello world project.
yeah you need to read through its templates and "source code" to understand what it does - which is not necessarily a bad thing for this type of project.
I have also struggled to find real world examples for these approaches.
Following a BDD approach with a coding CLI works a lot better, as it documents the features as code rather than verbose markdown files no one will read.
Having a checklist for an AI to follow makes sense, but that's why agents.md exists. Once the coding patterns and NFRs are documented in it, the agent follows them as well as they would follow a separate markdown spec.
This focus on markdown specs is the dumbest thing. Have a spec DSL that can be validated and transformed into real code. I've already got this working with CUE (you can even define gherkin rules as part of the spec and it'll codegen them), I just need to split the CLI out from the enterprise product it's embedded in.
I like the part that uses custom slash commands as way to wrap your input into some well-structured prompt template for given type of task. I like the part that also injects relevant pieces of "broken down AGENTS.md" as I see it.
I don't like the part that tries to leave no knot untied, which creates that sledgehammer for cracking a nut, as mentioned in the article. But I am sure it's easy to add another custom slash command like "/experiment" or "/stub" that would bring those context management benefits without the bloat, in situations when you don't know yet what and how you want to build something.
And then maybe "/wrap-up" to tie all the untied knots once you're sufficiently happy. Kinda like surgeon stepping aside after the core part of the operation.
Sharing my experience with SpecKit in case anyone finds it useful.
I've been using Speckit for the last two weeks with Claude Code, on two different projects. Both are new code bases. It's just me coding on these projects, so I don't mind experimenting.
The first one was just speckit doing its thing. It took about 10 days to complete all the tasks and call the job done. When it finished, there was still a huge gap. Most tests were failing, and the build was not successful. I had to spend an equally long, excruciating time guiding it on how to fix the tests. This was a terrible experience, and my confidence in the code is low because Claude kept rewriting and patching it with many fixes to one thing, breaking another.
For the second project, I wanted to iterate in smaller chunks. So after SpecKit finished its planning, I added a few slash commands of my own. 1) generate a backlog.md file based on tasks.md so that I don't mess with SpecKit internals. 2) plan-sprint to generate a sprint file with a sprint goal and selected tasks with more detail. 3) implement-sprint broadly based on the implement command.
This setup failed as the implement-sprint command did not follow the process despite several revisions. After implementing some tasks, it would forget to create or run tests, or even implement a task.
I then modified the setup and created a subagent to handle task-specific coding. This is easy, as all the context is stored in SpecKit files. The implement-sprint functions as an orchestrator. This is much more manageable because I get to review each sprint rather than the whole project. There are still many cases where it declares the sprint as done even though tests still fail. But it's much easier to fix, and my level of trust in the code is significantly higher.
My hypothesis now is that Claude is bed at TDD. It almost always has to go back and fix the tests, not the implementation. My next experiment is going to be to create the tests after the implementation. This is not ideal, but at this point, I'd rather gain velocity, since it would be faster for me to code it myself.
Really, we are doing waterfall, but with AI, now?
Plotly's new Plotly Studio product is a spec-anchored approach to building data applications. Each chart or dataset gets its own prompt/spec.
The question of how much detail to include in a spec is really hard. We actually split it into two levels - an input prompt describing details the user cares about in that component and an output spec describing what was built to allow verification.
At that point, isn’t it just a description of the chart or dataset?
In my experience with Kiro's spec-driven approach it generated massive task lists (12+ tasks with 4+ sub-tasks each). The workflow was decent but it deleted code unpredictably and wouldn't revert changes. Being a full IDE likely diverts resources to UI edge cases rather than core reliability.
So much simpler to just iterate without the puzzle box of tasks.
"a sledgehammer to crack a nut"
Seems I've been doing something like spec driven development on my last project. I keep a spec of the solution developed, and include it in every request sent to the ai, and it yields good results in my case. I'm still the developer in charge, but I can easily hand off non subtil or general code generation. It's clearly helped me code faster, though I had to spend quite some time on the spec, which still clarified a lot of things for me too. In the end I enjoy this approach.
I was excited to use spec-kit. I had to dump it eventually when it generated steps that were the equivalent of Tony Stark building a robot from scratch in a cave when "just screw this bolt on" would have sufficed.
Always made it too complex, and at some point it wasn't worth correcting it anymore.
I don't know why SDD suddenly became a thing, but FWIF, I find value in spec files to make sure I know what I'm going to get, and to track progress when I break up projects into smaller tasks. Mind you, I don't use any tool or framework; just a simple Markdown file. I don't see value in the formalism beyond that.
In my experiments with SpecKit I was always left wondering "when does it merge all this specs into a single ground truth". I never got there and it felt like a huge missing step.
Now I'm left trying to define/design what a "spec" for communication between humans and coding agents would look like, to power what Birgitta called spec anchored.
> Now I'm left trying to define/design what a "spec" for communication between humans and coding agents would look like, to power what Birgitta called spec anchored.
I feel that now with AI this is something that we have to finally do. Define how we write out a spec and record an architecture semi-formally, and in a way that is human-readable and human-manageable. And in a way that can 1) be consumed partially by an LLM context, rather than entirely (because it may be too big), and 2) have that partial ingestion be enough for it to do real work, either on the spec itself on or on the code, without deviating from the core intentions and architecture.
We tried and failed with the UML and Rational Rose type stuff, I think because it didn't record intentions well enough, was mostly pictures and not words, and seemed to be something that you would create after you finishd a project rather than fill in the details and guide you while you were building it. Hence, the whole idea fell away because it wasn't useful for anything but documentation, maintenance or refactoring; you were already selling the product before the spec became at all useful.
I'm left looking at vague leftfield ideas like https://c4model.com/.
> When I asked Kiro to fix a small bug (it was the same one I used in the past to try Codex), it quickly became clear that the workflow was like using a sledgehammer to crack a nut. The requirements document turned this small bug into 4 “user stories” with a total of 16 acceptance criteria, including gems like “User story: As a developer, I want the transformation function to handle edge cases gracefully, so that the system remains robust when new category formats are introduced.”
Kiro, your new corporate project manager.
they did train it on the amazon way