mlsanityData to 14 Oct 2026

Persona-Execution Separation: An Architecture Pattern for Evolving LLM Agents under Execution Audit

arXiv 2608.27427 ↗First posted 27 Aug 2026cs.SE cs.AIcited by 0

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

Abstract (arXiv)

Large language model (LLM) agents in governed organizations must let the persona (instructions, tone, self-presentation) evolve freely, while keeping execution (stateful, audited work) traceable. A single trust domain does not satisfy both cheaply. We present Persona-Execution Separation (PES): persona and execution reside in different trust domains, connected by a governed contract bridge. The persona is singly-homed and may drift; execution is faceless and audited. Status summaries may return; data bodies remain in the restrictive domain except a data-loss-prevention (DLP) exception; identity stays continuous. An approval matrix, DLP, and audit enforce the crossing. PES follows from three goals: free drift, execution traceability, and decoupling. Under LLM representational indistinguishability, any single-domain mechanism meeting all three must re-introduce typed change objects, an external gate, and a stable audit anchor: PES rebuilt at higher coupling cost. A development/pilot case in a regulated platform records five decisions over one month, four with rejected alternatives. A mechanism check found no execution-side re-validation under persona perturbation (five configurations) and no persona fingerprint on hard-asserted fields of completed runs. A controlled replication in regulated coding agents reproduced the separation under isolation across five models and four providers; bridge overhead was under 0.2% of end-to-end time in both environments. A probe of a pre-separation build found the execution path decoupled from the persona by omission, not by construction. The pattern applies when multi-user deployment, execution audit, and persona churn hold jointly.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 22 of its 47 references that are in the map. First posted on arXiv 27 Aug 2026.

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22 of 47 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026Proof-of-Execution Memory: Defending LLM Agents Against Forged-Reasoning Attacks by Verifying What Actually Happened17 Aug
  2. 2026Agentao: A Policy-Governed Runtime Harness for Embeddable Tool-Using LLM Agents4 Jul
  3. 2026Harness-MU: A Safe, Governed, and Effective Harness for Multi-User LLM Agents20 Jun
  4. 2025Progent: Securing AI Agents with Privilege Control16 Apr
  5. 2025Defeating Prompt Injections by Design24 Mar
  6. 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents24 Mar
  7. 2025Firewalls to Secure Dynamic LLM Agentic Networks3 Feb

Other references · 15

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  4. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  5. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  6. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  7. 2023Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulationcited by 214
  8. 2024AgentScope: A Flexible yet Robust Multi-Agent Platformcited by 121
  9. 2025Securing AI Agents with Information-Flow Controlcited by 121
  10. 2023Generative Artificial Intelligence for Software Engineering -- A Research Agendacited by 23
  11. 2026When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agentscited by 5
  12. 2026SafeGPT: Preventing Data Leakage and Unethical Outputs in Enterprise LLM Usecited by 3
3 more
  1. 2026Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systemscited by 3
  2. 2026Security in the Fine-Tuning Lifecycle of Large Language Models: Threats, Defenses,Evaluation, and Future Directionscited by 1
  3. 2026Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Productioncited by 1

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