New AI SDLC and how it works in your team
Long story short.
100+ engineers in a dev agency here. Multiple AI-native, from-scratch, completely custom projects turned AI-native. Portfolio of 30+ customers and products.
We have a different story on every product, depending on who, how, and how much our teams use LLMs in dev. More often than not, we ship a spaceship within the first 2-3 months and then bear the consequences.
Moved to Claude Code completely recently, from Cursor. Have built tons of skills, rules, and cross-org plug-ins. We run mostly senior teams.
Challenges we often face:
- Engineers get out of context. Losing track of what they are really building and what problem they are solving with the product.
- As a result, we often end up with tons of bugs as we sometimes build one thing while breaking the other.
- QAs get out of context quite often, too.
- PMs are barely catching up, so I sometimes have a feeling they just lose the big picture.
- UI/UX(Product Designers) are funny beasts too, as they start to eat into the FE work slowly as they learn Claude Code and are now shipping a good part of front-end:)
We are building fast, probably 2-3 times faster than before, and overall, the AI-first approach works, but I have a feeling there is a way to grow and improve, especially on large products where you need to manage and deliver tons of context and features with a huge codebase.
I have a feeling we are missing something on the documentation side, either during the requirements-shaping stage or as the product continues to grow.
Grateful for any insights into the team/Claude setups you run, quality gates for each stage of the SDLC, etc.