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Building reliable AI systems naturally creates organizational loopsHello, I’m yeTi.Recently, I have been seeing the word “loop” more often in discussions around AI agents.Reflection.Self-Refine.Self-improving agents.Loop Engineering.The underlying idea is usually similar.Instead of expecting an AI agent to produce the right answer in a single attempt, we let it inspect its own output, identify w..

From AI Coding Agents to AI Engineering OrganizationsIntroductionWhen I started the sqlgen-ai project, my goal was straightforward.I wanted to build an AI agent that could write code.At the time, I imagined a future where an AI developer could continuously improve itself, create tools it needed, and gradually become more capable over time.To explore that idea, I connected Hermes Agent, Codex, Gi..

Lessons from building a multi-agent AI development workflow for a production projectTL;DRBuilding a reliable AI coding agent is one engineering problem.Building a reliable AI development workflow with multiple agents is another.A single agent mostly struggles with execution quality.Multiple agents introduce coordination problems:task ownershipshared state visibilityrace conditionsworkspace conta..

Lessons from building reliable AI agent workflows with Hermes and local LLMsTL;DRMulti-agent systems become unstable when responsibilities overlapStronger models do not automatically improve workflow convergenceShared context without ownership boundaries creates execution driftSeparating Planner, Implementer, and Validator responsibilities significantly improved workflow stabilityThe Implementer..

Lessons from building and debugging a real-world multi-agent system with Hermes Agent📌 TL;DRThe agent didn’t fail to generate an answerIt failed to decide whether it should actMulti-agent systems require coordination signals, not just intelligenceThe fix was not better prompts, but explicit behavior contracts1. Problem — The Agent Didn’t Respond at AllWhile building a multi-agent system using H..

From Unpredictable AI to Reliable SystemsLessons from building real-world AI agent systems📌 TL;DRPrompt engineering alone was never enough for stable executionSingle-prompt systems created over-exploration, scope drift, and unreliable outputsI redesigned the system into Planner → Implementer → ValidatorReliability came from contracts, validation, and retry loops—not better promptsProduction rel..

From unpredictable AI outputs to production-ready LLM systems📌 TL;DRPrompt engineering improves output quality, but it does not guarantee reliabilityMost LLM systems fail in production because they cannot handle failureValidation layers, retry loops, and strict output contracts are what make AI automation reliableReliable AI agent systems are built with control systems, not just better promptsT..

From Unpredictable AI to Reliable SystemsLessons from Building Real-World AI Agent SystemsTL;DRPrompt engineering improves output quality, but it does not guarantee reliabilityLLM systems fail because outputs are probabilistic, not deterministicMulti-step AI agent pipelines amplify failure probabilitiesProduction-grade LLM systems require validation, retry loops, and convergence mechanismsReliab..

Lessons learned from building a real-world LLM coding agent with local models📌 TL;DRLLMs are non-deterministic → same input, different outputsPipeline architectures amplify failure probabilitiesPrompt engineering improves outputs but cannot guarantee reliabilityThe real solution is not better prompts, but convergence systems1. Problem — You Can’t Even Get Stable OutputsI wanted to build a local..

안녕하세요. yeti 입니다.오늘은 제가 실제로 운영 중인 AI 개발 환경과 AI 코딩 도구 운영 전략을 공유하려고 합니다.특히 이 글은 단순한 AI 도구 추천이 아니라,독립 개발자가 AI 개발 비용을 직접 감당하는 환경에서 어떤 기준으로 도구를 선택하는지에 대한 기록입니다.저는 모든 AI 도구 비용을 개인이 직접 지불합니다.그래서 이 글은 생산성 관점이 아니라 비용을 통제하면서 AI 코딩 도구를 사용하는 전략에 대한 이야기입니다.문제: AI 개발 비용은 생각보다 빠르게 증가한다AI 코딩을 적극적으로 활용하면 생산성은 확실히 올라갑니다.하지만 동시에 AI 토큰 비용과 구독 비용도 빠르게 증가합니다.2025년 11~12월, Cursor IDE 사용량이 늘어나면서 월 비용이 15 ~ 20만원까지 올라간 경험..