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Agent interfaces enable more effective self-evolving systems
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Beyond agent harnesses, I believe agent interfaces are an important direction for the development of self-evolving agents. This is connected to the idea of building for agents. You can pack so much into a good harness and interface that agents understand well. Think about the efficiencies you get. Costs you can reduce. More importantly, this lets agents work more effectively and reliably with interfaces that make sense for the task at hand. With Artifacts, we've already seen huge gains in…
DAIR.AI@dair_ai3d ago
Banger paper on evolving agent interfaces. Pay close attention to this new trend if you build with agents. Like optimizing harnesses, it's remarkable what you can achieve with agents that have access to better interfaces. Computer-use agents usually act through interfaces that people design by hand, so an agent may type a spreadsheet cell by cell when one line of code would do. Simular's Agent S4 has a meta-agent that builds those interfaces instead. It reads the task agent's trajectories, with no success labels, and writes new tools and notes on when to use them. With the evolved interfaces, an open 27B model reaches 21.3% on OSWorld V2, 3.8 times that of the same model with hand-built interfaces. That is above GPT-5.5 (13.0%) and Claude Opus 4.8 (20.6%), at about 4.3% and 1.5% of their cost. Population-based selection keeps the interface repositories that do best on validation tasks the meta-agent never sees, so the changes carry over to new tasks. On ScienceBoard, the evolved interfaces raise success from 41.4% to 58.6% with the same model. The code is open source. https://t.co/GugEMxwuzg Chat with Paper: https://t.co/5stmPhni6v