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"text": "Find the recording here: https://t.co/cWgyDGjqCG\n\nMore coming next week.",
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"text": "How to effectively run autonomous long-running coding agents?\n\nThis is one of the most exciting discussions on agents I've ever had.\n\nI recorded it and am making it freely available.\n\n(bookmark it)\n\nThe idea of autonomous long-running agents is a real thing.\n\nWe talk about lots of things like /goal, /loop, and dynamic workflows, and what comes next.\n\nOne interesting discussion was around how to make the agent run for longer while ensuring it stays on track.\n\nMost models today will struggle to coordinate work effectively. They sometimes pause the work early. Lots of mistakes happen, and lots of weird shortcuts (reward hacking).\n\nWhat helps is to be extremely clear about the goals it needs to achieve. To clarify the dos and don'ts clearly. Eliminate any assumptions you think the model would make. Deep expertise matters so much in this.\n\nBut you can get far through careful planning. My formula currently is to use Opus 4.8 for planning carefully and GPT-5.5 for all executions. For the evaluator (via /goal), I am often using something like Deepseek or the latest models from Qwen, Kimi, and MiniMax, etc.\n\nAnother insight we discussed to enforce goals is to provide strong visual cues for the agent to compare with. I found that a multimodal goal is a much stronger goal than a plain text one. And use agents to help you set clear goals.\n\nWatch here: https://t.co/ML3bSwGjUG",
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