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I built an AI dev team that reviews its own work — here's what I learned about multi-agent loops

Chris Lui 2026年08月01日 17:12 2 次阅读 来源:Dev.to

Most multi-agent demos are impressive for five minutes and useless for five hours. After months of building Task Hounds — an open-source, local multi-agent development workspace — here are the design decisions that actually mattered. The setup Task Hounds runs three agents in a loop around one project: A Manager that understands context, maintains the plan, and assigns exactly one concrete task per cycle A Worker that implements the task and files a structured report: files changed, test results, known issues A Reviewer that inspects the result for bugs, UX problems, and risks — before the Manager decides what happens next A human writes a Directive (the mission), and can inject thoughts or new tasks mid-run. Everything — plans, todos, reports, feedback, live agent streams — persists in local SQLite and renders in a real-time dashboard. Lesson 1: One task at a time beats parallel everything My first instinct was parallel workers. It demoed great and shipped nothing: agents stepped on each other's files and the Manager couldn't attribute failures. Serializing to one task per loop looks slower and finishes dramatically more work. Lesson 2: Give the human a write-protected anchor Goal drift is the silent killer of long loops. Around loop 10, the plan subtly stops resembling what you asked for. Our fix: the Human Directive is copied into every session and the loop is forbidden from editing it. Only a human can change the mission. Drift now shows up as visible divergence from a fixed anchor instead of quiet mutation. Lesson 3: Structured handoffs, not chat history Passing conversation history between agents fails in two ways: it blows the context window, and it lets downstream agents anchor on upstream reasoning noise. Every hop in Task Hounds is a fixed document: the Manager's memory is an explicit JSON handoff read once per loop; the Worker's output is a fixed report schema. If the machine-readable todo JSON is invalid, the loop repairs it before any work is released.

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