Autogenesis
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Static agents age quickly. As deployment environments change and new tools arrive, the agents that survive will be the ones that can safely rewrite themselves. This paper introduces Autogenesis, a self-evolving agent protocol where agents identify their own capability gaps, generate candidate improvements, validate them through testing, and integrate what works back into their own operational framework. No retraining and no human patching, just an ongoing loop of assessment, proposal, validation, and integration.
Two-layer protocol design: Autogenesis separates a Resource Substrate Protocol Layer (RSPL) that standardizes access to prompts, tools, environments, and memory from a Self-Evolution Protocol Layer (SEPL) that runs a Generate, Reflect, Improve, Evaluate, Commit loop over evolvable variables. The split keeps core capability registration stable while evolution happens on top.
Auditable lineage and rollback: Improvements are committed with version lineage, state access control, and reversible lifecycle operations. The protocol treats every self-modification as a first-class artifact that can be inspected, reproduced, or rolled back, which is what makes self-improvement safe enough to deploy.
Multi-agent applications: Autogenesis is demonstrated on multi-agent systems with planner, executor, and analyst roles. Agents evolve their own prompts, tool wrappers, and coordination routines using the shared protocol, showing that the abstraction is general enough to hold across roles rather than being tied to a single agent type.
Part of a broader self-improvement wave: The paper sits alongside Meta-Harness and the Darwin Gödel Machine as a concrete framework for operationalizing self-modification. Together they mark a shift from "agents that use tools" to "agents that edit their own tooling."
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