If you hand-tune agent harnesses, this one is worth your time. AutoDesign puts the harness itself inside the optimization loop. A meta-harne…

If you hand-tune agent harnesses, this one is worth your time. AutoDesign puts the harness itself inside the optimization loop. A meta-harness optimizer reads rollout feedback and directs a code agent to rewrite the harness, round after round. They test it on paper-to-poster generation with PosterBench, 100 papers across five disciplines. AutoDesign scores 78.32 against 70.87 for the closed-source Claude Design. Dropping the learned DesignHarness into seven other code-agent-model configurations lifts the average from 54.99 to 67.39, so what it learned is scaffold knowledge rather than a fit to one model. One full autonomous run executes 253 tool calls and 11 editing turns in 40 minutes for under $3. Paper: https://arxiv.org/abs/2608.13560 Track more trending AI papers in our academy: https://academy.dair.ai/

// HeavySkill // One of the cleaner takes on agentic harness design I've read. They argue that what actually drives agent harness performanc…
New research with Microsoft's and colleagues on training agents inside the harnesses they actually run in. (bookmark it) Why it matters: Age…
Harness choice is a big deal. So much room to advance and improve results across the board with agent harnesses. Great paper highlighting th…
Great paper on self-improving agent harnesses. (bookmark it) If you maintain a production agent harness, finding every file behind one behav…
Dynamic workflows (generating harnesses on the fly) are a new form of test-time compute. But LLMs aren't great at building them. I often hav…

Source: DAIR.AI (X) | 2026-08-14

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