You don't need spec kit or humanlayer per se, though they are good reference points for starting out, but you do need your own CLAUDE/AGENTS, README, ARCHITECTURE, SDRs, policies, research docs and planning docs all nicely organized in files and folders to work well with AI agents.
Particularly in a brownfield development context, using AI to research issues, gaps, bugs, tech debt, reusable patterns etc. as research files is really useful. I find the sweet spot is ~750 lines for each file. Planning files can max out at 1,500 lines if needed or otherwise broken up into individual phases. You can always ask an LLM to create a starter set of docs to get you going and then maintain as you go along.
For the management nerds, none of this is new. See: Writing Effective Use Cases by Alistair Cockburn and Agile Specification-Driven Development by Ostroff, Makalsky and Paige. The fundamentals remain the same and I'd say become even more important in a brownfield context with a high degree of tech debt or complexity. Greenfield is a different story.
What's important with spec driven development is that the LLMs dramatically reduce the cost of both good documentation and good technical specifications. Once you have a good plan and good references, you can build anything with a high degree of accuracy.
I'd add caution about drift. The more documentation you shove into the context, the worse things can get. You can also create a beautiful and perfect functional spec that becomes a swiss cheese of gaps when you create your technical implementation plan. Always check and use different AI models adversarially to ensure you are actually getting the plan you want. Usually ChatGPT can spot Claude's blind spots, for example. And always test manually.
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