The author describes an innovative approach to automating code generation and improvement using AI agents. Their workflow involves multiple “claude code” environments, with O3 and Sonnet 4 creating, executing, and verifying plans, while Sonnet 3.7 handles simpler tasks. The guiding principle is to focus on fixing inputs, rather than outputting results, streamlining debugging and allowing agents to improve through iterative feedback. The “factory” of agents autonomously generates and refines code, applying specific internal style rules, while addressing common issues systematically. By employing parallel processing, agents can learn from failures and adapt their plans accordingly. The author aims to enhance coordination among agents, align business documentation with their processes, and develop more complex workflows. Ultimately, the system is efficient enough for rapid code delivery, but the author acknowledges further improvements are necessary for full automation. The central mantra remains: prioritize fixing inputs over outputs to cultivate a self-improving code generation environment.
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