Ouroboros: A Self-Developing Frontier Coding Agent with Reviewed Core Evolution

Long-horizon agents are model–harness systems, yet most harnesses remain fixed after design. We present Ouroboros 1 – a selfdeveloping agent harness whose tools, context assembly, prompts and core implementation improve through reviewed commits that become the runtime for later work. Core evolution proceeds in two modes. In recursive free evolution, improvement is itself a task and completion can schedule the next evolution cycle. In experience-driven core evolution, ordinary work and social interaction expose bugs, rough edges, and inefficient context construction leading to reviewed structural changes. On Terminal-Bench 2.1, an Opus 5 run scores 86.97% (86.74% after trajectory audit), the best result reported on this benchmark. An Opus 5 run on OSWorld-Verified reaches 90.69%, above the best previously reported score, and a five-rollout CL-Bench campaign sets a new state of the art at 0.2301. Hope is the longest-running publicly documented Ouroboros deployment: a 161-day living-agent experiment in free evolution under governed human communication across seven surfaces, where people surface faults and proposals but the agent decides which changes to pursue.

Introduction. Agent scores on long-horizon benchmarks are products of the base model, the execution harness, the environment, and the grader. As models improve, an increasing share of realized capability is determined by how the harness assembles context, invokes tools, verifies outcomes, and recovers from failure. Most production harnesses freeze these policies after design. Ouroboros instead treats the harness as an evolving object: its source, prompts, tools, review logic, and core implementation live in a versioned repository and change through a reviewed commit path that becomes the substrate for subsequent tasks. This self-development has two modes. Recursive free evolution makes improvement itself a task. After inspecting the current system, the agent selects and implements a change, and completion can schedule another evolution cycle, yielding a continuing sequence of reviewed updates rather than a fixed optimization run. Experience-driven core evolution begins with ordinary work.

Discussion / Conclusion. Ouroboros shows that a reviewed, self-modifiable harness can set new state-of-the-art results on Terminal-Bench 2.1, OSWorld-Verified, and CL- Bench while matching frontier coding harnesses on SWE-bench Pro and GAIA. Experience-driven core evolution turns ordinary work into improvements of the agent itself: observed bugs, rough edges, context failures, and social feedback become reviewed changes to the harness that receives later tasks. Hope demonstrates this mechanism during months of sustained human interaction across seven communication surfaces. The operational safety architecture addresses the corresponding risk: an agent that can improve its own code and select its own model APIs requires control boundaries that remain authoritative under evolutionary pressure. Source, adapters, methodology, submissions, and public traces accompany the report.