mlsanityData to 7 Oct 2026

OpenKedge: Governing Agentic Mutation with Execution-Bound Safety and Evidence Chains

arXiv 2604.08601 ↗First posted 7 Apr 2026cs.AI cs.LGcited by 4

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

The rise of autonomous AI agents exposes a fundamental flaw in API-centric architectures: probabilistic systems directly execute state mutations without sufficient context, coordination, or safety guarantees. We introduce OpenKedge, a protocol that redefines mutation as a governed process rather than an immediate consequence of API invocation. OpenKedge requires actors to submit declarative intent proposals, which are evaluated against deterministically derived system state, temporal signals, and policy constraints prior to execution. Approved intents are compiled into execution contracts that strictly bound permitted actions, resource scope, and time, and are enforced via ephemeral, task-oriented identities. This shifts safety from reactive filtering to preventative, execution-bound enforcement. Crucially, OpenKedge introduces an Intent-to-Execution Evidence Chain (IEEC), which cryptographically links intent, context, policy decisions, execution bounds, and outcomes into a unified lineage. This transforms mutation into a verifiable and reconstructable process, enabling deterministic auditability and reasoning about system behavior. We evaluate OpenKedge across multi-agent conflict scenarios and cloud infrastructure mutations. Results show that the protocol deterministically arbitrates competing intents and cages unsafe execution while maintaining high throughput, establishing a principled foundation for safely operating agentic systems at scale.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 11 of its 22 references that are in the map. First posted on arXiv 7 Apr 2026.

Builds on

11 of 22 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Decentralized Multi-Agent System with Trust-Aware Communication2 Dec
  2. 2025The Trust Paradox in LLM-Based Multi-Agent Systems: When Collaboration Becomes a Security Vulnerability21 Oct

Other references · 9

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  3. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  4. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  5. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  6. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  7. 2016Concrete Problems in AI Safetycited by 1,582
  8. 2023ChatDev: Communicative Agents for Software Developmentcited by 893
  9. 2026Autonomous Action Runtime Management(AARM):A System Specification for Securing AI-Driven Actions at Runtimecited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Sovereign Execution Broker: Enforcing Certificate-Bound Authority in Agentic Control Planes
  2. 2026OpenAgentFlow: Enabling System-Wide Safety Boundaries for Heterogeneous AI Agent Fleets
  3. 2026Clawed and Dangerous: Can We Trust Open Agentic Systems?
  4. 2026ElephantAgent: Contextual State Continuity in Agentic Systems
  5. 2026Securing LLM Agents Need Intent-to-Execution Integrity
  6. 2026Architectural Obsolescence of Unhardened Agentic-AI Runtimes
  7. 2026Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw
  8. 2026Faramesh: A Protocol-Agnostic Execution Control Plane for Autonomous Agent Systems