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Inside One Engineer's Journey to Master Long-Running Agents
Long-running AI coding agents achieve better consistency by treating their research, planning, and verification as persistent, state-machine-backed artifacts.
What: Engineers at DoorDash addressed the limitations of short-lived agents by building an 'Agentic Orchestrator' that manages long-running workflows. By forcing agents to ground their progress in structured, shared data—such as research notes and verification logs—they maintain context during multi-step coding tasks.
Why it matters: Reliability for complex agentic workflows remains the primary barrier to adoption. Moving from stateless, prompt-response cycles to persistent, state-driven architectures is the current standard for production-grade coding agents.
Original article
Long-running coding agents work better when they are grounded in structured context and persistent artifacts: repo research, knowledge bases, plans, progress files, verification reports, and review notes. Agentic Orchestrator turns those handoffs into a state-machine workflow, giving agents enough shared “data” to tackle bigger tasks without losing coherence.
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