mlsanityData to 7 Oct 2026

Execution Envelopes: A Shared Admission Contract for Backend AI Execution Requests

arXiv 2605.08267 ↗First posted 8 May 2026cs.SE cs.AI cs.DCcited by 0

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

Abstract (arXiv)

Enterprise AI backends increasingly admit heterogeneous execution requests across model deployment, inference, evaluation, data movement, and agentic workflows. In many systems, those requests arrive in service-specific shapes, which makes it difficult to attach shared admission-time behavior such as logging, governance hints, resource accounting, authorization-aware policy hooks, and later runtime review without rebuilding the same contract in each subsystem. This paper introduces the execution envelope, a normalized internal admission object that records who is asking for what kind of execution, what resources were requested, what policy-relevant scope accompanied the request, and what the backend ultimately granted. The proposal is intentionally narrow. It does not replace service-specific request models, perform scheduling, or introduce a new authority token. Instead, it defines a descriptive admission seam that can be threaded through real backend paths before backend-specific resolution begins. I formalize the distinction between requested and granted resources, specify the field families, invariants, and lifecycle of the envelope, work through POST /serving/deploy_model as an initial proving ground, and position the design relative to usage control, analyzable authorization, admission control, and cluster scheduling. The central claim is that a shared execution-admission contract is a useful missing primitive for modern AI backends because it creates one place to attach governance and observability without pretending to solve placement, policy, and runtime execution in a single step.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 4 of its 12 references that are in the map. First posted on arXiv 8 May 2026.

Builds on

4 of 12 references.

Closest ancestors · 2

Same direction, newest first

  1. 2026Authorization Propagation in Multi-Agent AI Systems: Identity Governance as Infrastructure6 May
  2. 2026Partial Evidence Bench: Benchmarking Authorization-Limited Evidence in Agentic Systems6 May

Other references · 2

  1. 2024Cedar: A New Language for Expressive, Fast, Safe, and Analyzable Authorization (Extended Version)cited by 31
  2. 2025Scorpio: Serving Right Requests at the Right Time for Heterogeneous SLOs in LLM Inferencecited by 6

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Similar, not cited

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  5. 2026Architectural Implications of Agentic AI Workflows
  6. 2026A Five-Plane Reference Architecture for Runtime Governance of Production AI Agents
  7. 2026EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises
  8. 2026LeaseGuard: Incumbent-Preserving Admission Control for Privileged LLM Agents