AI agents are evolving from answer engines into persistent teams that utilize tools, delegate tasks, learn from experience, and modify artifacts shaping their future behavior. The defining question for deployment is no longer merely what agents can do, but who controls what they are allowed to become.\n\nWe introduce LOGOS, a pluggable layer for self-evolution and governance that strengthens existing multiagent frameworks rather than replacing them. LOGOS compiles heterogeneous multimodal inputs, including documents, images, audio, tables, databases, APIs, and human instructions into versioned agent packs containing agents, tools, knowledge, tests, permissions, and policies.\n\nDuring operation, it transforms agent activity into portable, auditable event traces and applies fail-closed verification across frameworks and backends. Every learned prompt, memory, skill, tool, role, or workflow remains an untrusted release candidate until held-out execution evidence, human-controlled policy, and explicit authorization permit its promotion.\n\nThis architecture enables "verifiable human-agent loop engineering": agents can act, ask, learn, and propose improvements, while humans can steer objectives, permissions, approvals, and irreversible actions without interrupting continuous operation. LOGOS provides a living logic for accountable automation. Agents may evolve at machine speed, but only evidence and human authority can close the loop.\n\nBlogger's Review: LOGOS introduces self-evolution and governance mechanisms that open up new possibilities for AI agents' intelligence and human-agent collaboration. This framework not only enhances the flexibility of existing systems but also ensures human core control during the evolution of agents, reflecting the governance needs of future intelligent systems.