People long believed in human-like computer use. But software (even terminals) is built for humans, not AI agents. Why not let agents build their own? Meet 𝗔𝗴𝗲𝗻𝘁 𝗦𝟰, an agent scientist evolving its own interfaces, letting an open 27B model beats GPT-5.5 & Opus 4.8. With its evolved interfaces, the same model improves 𝟯.𝟴× and costs <𝟱% as much per task as either frontier model. Agents today read screenshots and act through click, scroll, type, maybe a shell. So an agent fills a spreadsheet cell by cell when one line of code would do, or prints two thousand lines of a file to find one function. Every new app or model release means a human reads trajectories, diagnoses failures, and hand-writes new primitives. Skill-distillation methods can reorder what the interface allows, but they can’t extend it. Agents compensate with more test-time compute instead of a better interface. 𝗠𝗼𝗱𝗲𝗹𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲; 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 𝘀𝘁𝗮𝘆 𝗳𝗿𝗼𝘇𝗲𝗻. “We want AI agents that can discover like we can, not which contain what we have discovered.” — Rich Sutton, The Bitter Lesson Hand-designed interfaces are what we have discovered. Agents can read their own trajectories, spot what’s missing, and write and test code. 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆. That’s what Agent S4 does. Agent S4 makes a 𝗿𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆 𝗼𝗳 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 𝗮𝗻𝗱 𝘂𝘀𝗮𝗴𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 the unit of evolution. A task agent attempts synthetic tasks with no success labels or verifiers. A meta-agent studies the trajectories, forms hypotheses about which actions, observations, or knowledge would help, and builds descendants through mutation or crossover. Validation tasks the meta-agent never sees decide which repositories survive. The model and harness stay fixed, and the meta-agent 𝗻𝗲𝘃𝗲𝗿 𝘀𝗲𝗲𝘀 𝗮 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝘁𝗮𝘀𝗸. 𝗢𝗻𝗹𝘆 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗲𝘃𝗼𝗹𝘃𝗲𝘀. With the evolved repository, Qwen3.8-27B goes from 𝟱.𝟲% to 𝟮𝟭.𝟯% on OSWorld V2, 𝟯.𝟴× the same model with fixed interfaces and a new state of the art for open-source computer-use agents. It beats GPT-5.5 (13.0%, $25.50/task) and edges past Opus 4.8 (20.6%, $72.40/task) at $𝟭.𝟭𝟬 per task. On ScienceBoard, the same model goes from 41.4% to 58.6%. The analysis is the more interesting part. Evolution mostly deepens interfaces the agent already has: 4 new interfaces and 127 extensions. Give the meta-agent a scratch VM to test its code, and it builds 12 new interfaces and 253 extensions, reaching music scores, audio projects, and 3D scenes. The best repository came from crossing two earlier branches. In one case study, the seed agent hand-builds a browser client mid-task and debugs through failure after failure; the evolved agent calls page_text and reaches the same message at tool call 𝟭𝟭 instead of 𝟯𝟳. Human-like computer use was a starting point, not the destination. The interface an agent acts through is a second axis of improvement, beyond better models and harnesses, and agents can now design it themselves. 𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 𝗶𝘀 𝗮 𝗻𝗲𝘄 𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁 𝘀𝗲𝗹𝗳-𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁.

𝐁𝐞𝐭𝐭𝐞𝐫 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 & 𝐥𝐨𝐰𝐞𝐫 𝐜𝐨𝐬𝐭, 𝐚𝐬 𝐬𝐨𝐨𝐧 𝐚𝐬 𝐲𝐨𝐮 𝐩𝐮𝐭 𝐟𝐫𝐨𝐧𝐭𝐢𝐞𝐫 𝐚𝐠𝐞𝐧𝐭𝐬 𝐨𝐧 𝐋𝐄𝐀𝐒𝐇! 🦮 Why use a frontier model at every step when a lightweight agent can handle lots of the work? Introducing 𝐋𝐄𝐀𝐒𝐇: 𝐔𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐲-𝐀𝐰𝐚𝐫𝐞 𝐖𝐞𝐚𝐤-𝐭𝐨-𝐒𝐭𝐫𝐨𝐧𝐠 𝐄𝐬𝐜𝐚𝐥𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐎𝐯𝐞𝐫𝐬𝐢𝐠𝐡𝐭. LEASH unleashes the strong agent only when the weak agent's latent uncertainty signals the need, and keeps it on a leash afterward. Two key challenges: 1️⃣ 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐞𝐬𝐜𝐚𝐥𝐚𝐭𝐞? Use the weak agent's latent uncertainty as an inexpensive signal that can be monitored at every step. 2️⃣ 𝐇𝐨𝐰 𝐭𝐨 𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧 𝐨𝐯𝐞𝐫𝐬𝐢𝐠𝐡𝐭 𝐚𝐟𝐭𝐞𝐫 𝐞𝐬𝐜𝐚𝐥𝐚𝐭𝐢𝐨𝐧? Even frontier agents can go astray. And when served through black-box APIs, their internal states are inaccessible. LEASH enables continued oversight without accessing the strong agent's internals. 𝐓𝐡𝐞 𝐤𝐞𝐲 𝐢𝐧𝐬𝐢𝐠𝐡𝐭: Knowing when to escalate—and when to intervene—is just as important as knowing how to act. The future of agentic AI isn't about using the strongest model everywhere. 𝐈𝐭'𝐬 𝐚𝐛𝐨𝐮𝐭 𝐝𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐰𝐡𝐞𝐫𝐞 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐬𝐭.

There are plenty of “human-pretending-to-be-an-agent” game demos out there. This one is real. Sai + Jev played Minecraft, fought mobs, and spawned mining sub-agents—all in real time. 1 hour. $0.01. And Sai can spin up 100× agents in parallel for under $1 total. #RoboSecretary #SaiFleet