Faramesh: A Protocol-Agnostic Execution Control Plane for Autonomous Agent Systems
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Abstract (arXiv)
Autonomous agent systems increasingly trigger real-world side effects: deploying infrastructure, modifying databases, moving money, and executing workflows. Yet most agent stacks provide no mandatory execution checkpoint where organizations can deterministically permit, deny, or defer an action before it changes reality. This paper introduces Faramesh, a protocol-agnostic execution control plane that enforces execution-time authorization for agent-driven actions via a non-bypassable Action Authorization Boundary (AAB). Faramesh canonicalizes agent intent into a Canonical Action Representation (CAR), evaluates actions deterministically against policy and state, and issues a decision artifact (PERMIT/DEFER/DENY) that executors must validate prior to execution. The system is designed to be framework- and model-agnostic, supports multi-agent and multi-tenant deployments, and remains independent of transport protocols (e.g., MCP). Faramesh further provides decision-centric, append-only provenance logging keyed by canonical action hashes, enabling auditability, verification, and deterministic replay without re-running agent reasoning. We show how these primitives yield enforceable, predictable governance for autonomous execution while avoiding hidden coupling to orchestration layers or observability-only approaches.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 3 of its 20 references that are in the map. First posted on arXiv 25 Jan 2026.
Builds on
3 of 20 references.
References · 3
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Citing papers per year: 2026: 4.
4 papers.
- 2026SecureClaw: Clawing Back Control of LLM Agentscited by 2
- 2026The Importance of Out-of-Band Metadata for Safe Autonomous Agents: The Redpanda Agentic Data Planecited by 1
- 2026Governed Auditable Decisioning Under Uncertainty: Synthesis and Agentic Extensioncited by 0
- 2026Janus: Evidence-Before-Effect Sagas and Offline-Verifiable Provenance for Agentic LLMscited by 0
- 2026Janus: Evidence-Before-Effect Sagas and Offline-Verifiable Provenance for Agentic LLMs29 Sep 2026
- 2026SecureClaw: Clawing Back Control of LLM Agents8 Jun 2026
- 2026The Importance of Out-of-Band Metadata for Safe Autonomous Agents: The Redpanda Agentic Data Plane27 May 2026
- 2026Governed Auditable Decisioning Under Uncertainty: Synthesis and Agentic Extension21 Apr 2026
Similar, not cited
Close in wording, no citation either way
- 2026CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems
- 2026Behavioral Governance for Autonomous AI Agents: The AgentBound Framework
- 2026Sovereign Execution Broker: Enforcing Certificate-Bound Authority in Agentic Control Planes
- 2026Verifiable Agentic Infrastructure: Proof-Derived Authorization for Sovereign AI Systems
- 2026When Tool Outputs Become Commands: Separating Action Induction from Runtime Authorization in Tool-Augmented LLM Agents
- 2026SARC: A Governance-by-Architecture Framework for Agentic AI Systems
- 2026Runtime Authorization Consistency Checking for MCP-based Agentic Workflows
- 2026Runtime Governance for AI Agents: Policies on Paths