The Feedback Loop in AI SDLC
The article discusses the importance of feedback loops in the AI software development lifecycle, emphasizing the balance between autonomous execution and oversight through review gates. It introduces two modes of operation—'Iterate' for supervised tasks and 'Mission' for parallel, autonomous work—both relying on a shared system of checks and knowledge. The feedback loop ensures code quality and system trustworthiness even when developers are not actively involved in every step.
- ▪The feedback loop in AI SDLC consists of autonomous execution and review gates that validate code before merging.
- ▪Two operational modes, 'Iterate' and 'Mission', define how work is structured and supervised based on task complexity and autonomy.
- ▪Review gates check for architecture integrity, code design, layout, logic, and flow correctness to prevent undercooked outputs.
- ▪Both modes use the same knowledge base, branch rules, and feedback mechanisms to maintain consistency and traceability.
- ▪The system relies on specialized agents like harness-lead and harness-worker, each with defined roles and isolation protocols to ensure reliable task execution.
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The Feedback LoopAutonomous execution, review gates, and how corrections flow back into the systemLuka LeskovsekApr 28, 20261ShareAt Makers & Breakers, I write about what it actually takes to build products — the engineering, the leadership, the systems thinking. These pieces come from real projects and real mistakes. Some challenge assumptions. Most just try to be honest about what I’ve seen work and what hasn’t.This is Part 4 of a series. If you missed the earlier parts: Part 1: The Narrow Landing Surface · Part 2: The Context Tax · Part 3: The Orchestration Layer.Wednesday morning. Five PRs in my queue from missions that ran overnight. The reviewer flagged two of them.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Substack.
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