Published on: May 2026
CAPFENCE: REDUCING COMPUTATIONAL OVERHEAD IN AGENTIC AI VIA SELECTIVE AUTHENTICATED DELEGATION
Anjali Srivastava Piyush Gupta
Payal Gulati
J.C. Bose University of Science and Technology YMCA Faridabad India
Article Status
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Abstract
This paper proposes CapFence, a lightweight runtime delegation layer that addresses this gap through four tightly integrated components: (i) a capabil-ity compiler that translates natural-language intent into structured, least-privilege capability tokens; (ii) a com-pact risk gate that scores each pro-posed action using a low-cost calibrated model; (iii) a selective escalation mecha-nism that reserves expensive verification for genuinely ambiguous actions; and
(iv) a compressed audit chain that sup-ports retrospective accountability with-out verbose trace retention. Motivated by the authenticated-delegation prob-lem articulated by South et al. [1] and the deployment-visibility tradeoffs doc-umented by Chan et al. [2], CapFence targets simultaneous reductions in de-cision latency (≈75%), token cost over-head (≈77%), false-positive rate (≈46%), and peak memory footprint relative to an always-on LLM monitor, while re-taining task success on the GAIA, We-bArena, Mind2Web, VisualWebArena, and AgentBench benchmark suites. The primary contribution is a governance primitive that makes least-privilege del-egation both principled and practically deployable.
Keywords: Agentic AI, authenticated delegation, runtime gating, capability to-kens, selective escalation, auditability, edge deployment.
How to Cite this Paper
Srivastava, A. & Gupta, P. (2026). CapFence: Reducing Computational Overhead in Agentic AI via Selective Authenticated Delegation. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.326
Srivastava, Anjali, and Piyush Gupta. "CapFence: Reducing Computational Overhead in Agentic AI via Selective Authenticated Delegation." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.326.
Srivastava, Anjali, and Piyush Gupta. "CapFence: Reducing Computational Overhead in Agentic AI via Selective Authenticated Delegation." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.326.
References
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Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: May 10 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.
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