LOGOS: A Living Logic for AI Agent Teams That Evolve With Humans
Yuma Ichikawa, Yamato Arai, Kosaku Kimura, Akira Sakai, Hiromichi Kobashi
Why It Matters
What makes this one worth your time
This framework addresses the critical challenge of controlling the evolution of AI agents, ensuring they remain aligned with human values and objectives as they learn and adapt.
LOGOS enables accountable evolution of AI agent teams with human governance.
Summary
The paper introduces 'logos', a governance framework for AI agent teams that allows for self-evolution while maintaining human oversight and accountability through versioned agent packs and fail-closed verification.
Key contributions
- Introduction of a pluggable governance layer for AI agents.
- Development of versioned agent packs that include tools, knowledge, and policies.
- Implementation of a system for creating auditable event traces during agent operation.
Notable insights
- The architecture allows for the integration of diverse input types into a cohesive governance model.
- The concept of 'fail-closed verification' suggests a proactive approach to maintaining safety in evolving AI systems.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.10878v1 Announce Type: new Abstract: AI agents are evolving from answer engines into persistent teams that use tools, delegate work, learn from experience, and modify the artifacts that shape 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. We 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. During 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. This 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.