mlsanityData to 7 Oct 2026

Governed Evolution of Agent Runtimes through Executable Operational Cognition

arXiv 2605.27328 ↗First posted 26 May 2026cs.SE cs.AI cs.MAcited by 2

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Recent advances in agentic systems increasingly treat code as an executable operational substrate rather than as a disposable output artifact. Prior work such as 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 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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 21 references that are in the map. First posted on arXiv 26 May 2026.

Builds on

13 of 21 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models7 Nov
  2. 2024MapCoder: Multi-Agent Code Generation for Competitive Problem Solving18 May

Other references · 11

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  3. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  4. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,172
  5. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  6. 2022PAL: Program-aided Language Modelscited by 635
  7. 2025Agentic Context Engineering: Evolving Contexts for Self-Improving Language Modelscited by 349
  8. 2026Meta-Harness: End-to-End Optimization of Model Harnessescited by 248
  9. 2023Chain of Code: Reasoning with a Language Model-Augmented Code Emulatorcited by 120
  10. 2026AutoHarness: improving LLM agents by automatically synthesizing a code harnesscited by 63
  11. 2026UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experiencecited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
  2. 2026openJiuwen: Beyond Static Harnesses for Long-Horizon Coding Agents
  3. 2026MOSS: Self-Evolution through Source-Level Rewriting in Autonomous Agent Systems
  4. 2026Argus: A General-Purpose Agentic Reasoning Runtime for Long-Horizon Tasks
  5. 2026Architecting AgentOps Needs CHANGE
  6. 2026HarnessForge: Joint Harness and Policy Evolution for Adaptive Agent Systems
  7. 2026A Contract-Centered Architecture for Scalable and Manageable Agentic Runtimes
  8. 2026Code as Agent Harness