Developer's Two-Year Detour Building AI Agent Scaffolding Pays Off Overnight

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A software developer spent two years building the underlying infrastructure for autonomous AI agents, rather than the agents themselves. Last night, two agents independently merged eleven pull requests while the developer slept, including a 6,500-line feedback pipeline built across nine PRs. The developer draws on the MAST taxonomy — derived from over 1,600 annotated traces across seven frameworks — which identifies fourteen failure modes in multi-agent systems, arguing that trustworthiness must precede autonomy. The core insight is that most long-horizon agent failures stem from absent scaffolding, not model capability. The developer credits multiple community contributors, including Jesse Vincent's brainstorm-design-plan pipeline, as foundational to the architecture built.

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