Disclosure: I’m the builder. Most AI coding tools use a single agent in a loop: user prompts → agent generates → user reviews → agent iterates. This works for small tasks but breaks down because the agent accumulates irrelevant context, can’t parallelize, and has no structural gates for quality. I built Swim Code (swimcode.ai) around multi-stage pipelines where each stage has a specialized agent with typed context allocation. The planning agent receives architecture context. The coding agent receives acceptance criteria. The testing agent receives only the code. Observations: Scoped context consistently produces better output than full context dumps. Bounded retry loops resolve ~70% of test failures without human intervention. Git worktree isolation per task enables true parallel execution (3-5). Main failure mode is lossy context summarization in certain edge cases. Model-agnostic: Claude, GPT, Ollama (experimentally). Desktop app, runs locally submitted by /u/Warmaster0010
Originally posted by u/Warmaster0010 on r/ArtificialInteligence