Press Space to continue
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Press Space to continue
Press Space to continue
Great video about how to build long-running agents that perform. 1) Before any code gets written, spend 80% of your time building the artifacts the repo will need: architecture, conventions, references, runbooks. 2) Two repos instead of one. One holds the code. One holds the documentation: epics, specs, logs, scratchpads. A CLI sits between them and keeps them in sync. 3) Epic, then spec, then ticket. The epic sets scope boundaries and milestones. It breaks into project specs, which break…
If you think you're AI Native, here's a good test: When you're heading to bed tonight, are you asking yourself "what agents can I be running overnight to get a head start on my day tomorrow?" I posted that last week and hundreds commented on it. The most common pushback was some version of "congrats, you've automated slop." Here's my take: if your overnight runs produce slop, that's a process failure, not an agent failure. The model did exactly what you prepared it to do. You just didn't prepare it. And that's actually good news, because process is something you can level up. At @tenex_labs we run agents this way every day, on real client systems, using an internal framework we call MetaHarness. Before any long run starts, the plan is written down, the work is broken into small tasks, every task carries pass/fail success criteria, and guardrails define what the agent can touch and when it has to stop and wait for a human. With that in place, the agent isn't wandering your codebase all night. It's working inside rails you laid, and the only things waiting for you in the morning are the decisions that needed a human. Most people are still prompting and praying. This is your chance to build the framework that takes your agents to the next level. I recorded the whole process in a new video for you all ↓