Governed Evolution of Agent Runtimes through Executable Operational Cognition
2026-05-26 • Software Engineering
Software EngineeringArtificial IntelligenceMultiagent Systems
AI summaryⓘ
The authors discuss how software agents can treat their own code as reusable tools that live and change while the system runs, instead of as one-time outputs. They propose a system called HarnessMutation that carefully manages how these agent tools evolve, making sure changes are tested, tracked, and reversible. This approach makes the agents' self-changes controlled and observable, rather than random or unrestricted. The authors also explain how to apply these ideas using current technology to build adaptable but reliable multi-agent systems.
agentic systemsruntime adaptationexecutable codemulti-agent systemscode evolutionlifecycles managementtraceabilityrollbackoperational substrategovernance
Authors
Mariano Garralda-Barrio
Abstract
Recent advances in agentic systems increasingly treat code as an executable operational substrate rather than as a disposable output artifact. Prior work such as \emph{Code as Agent Harness} frames validated agent-generated artifacts as runtime entities that can be created, executed, revised, persisted, and reused within long-running cognitive loops. However, the governance, lifecycle management, and operational evolution of such artifacts remain under-specified. This paper proposes a framework for governed runtime evolution in multi-agent systems through executable operational cognition. We formalize agent-generated artifacts as persistent runtime capabilities that progressively become part of the operational substrate rather than transient intermediate outputs. Building on this perspective, we introduce \emph{HarnessMutation} as a governed mechanism for lifecycle-aware runtime adaptation operating under explicit validation, traceability, evaluation, and rollback constraints. Rather than treating runtime adaptation as unrestricted self-modification, the proposed framework models evolution as a bounded and observable process over persistent operational memory. It further shows how these ideas can be operationalized over modern agent runtimes and governance-oriented orchestration systems, providing a conceptual foundation for adaptive infrastructures whose evolution remains explicit, auditable, and constrained.