| I heard an interesting story from an architect at a large software consultancy. They are using AI in their teams to manage legacy codebases in multiple languages. TLDR; it works for a codebase of 1M LoC. AI writes code a lot faster, completing tasks in days instead of sprints. Tasks can be parallelized. People code less, but they need to think more often. (1) Maintain clear and structured architecture documentation (README, DDD context/module descriptions files, AGENTS-MD). (2) Create detailed implementation plans first - explicitly mapping dependencies, tests, and potential challenges. (3) Treat the implementation plan as a single source of truth until execution finishes. Review it manually and with LLM-assistance to detect logical inconsistencies. Plan is easier to change, than a scattered diff. (4) In complex cases - instruct AI agents about relevant documents and contexts before starting tasks. (5) Approve implementation plans before allowing AI to write code (6) Results are better if code agent can launch automated full-stack tests and review their outputs in the process. The same works for me in smaller projects. Less ceremony is needed there. |