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VIGIL: Towards Edge-Extended Agentic AI for Enterprise IT Support

Sarthak Ahuja, Neda Kordjazi, Evren Yortucboylu, Vishaal Kapoor, Mariam Dundua, Yiming Li, Derek Ho, Vaibhavi Padala, Jennifer Whitted, Rebecca Steinert

Abstract

Enterprise IT support is constrained by heterogeneous devices, evolving policies, and long-tail failure modes that are difficult to resolve centrally. We present VIGIL, an edge-extended agentic AI system that deploys desktop-resident agents to perform situated diagnosis, retrieval over enterprise knowledge, and policy-governed remediation directly on user devices with explicit consent and end-to-end observability. In a 10-week pilot of VIGIL's operational loop on 100 resource-constrained endpoints, VIGIL reduces interaction rounds by 39%, achieves at least 4 times faster diagnosis, and supports self-service resolution in 82% of matched cases. Users report excellent usability, high trust, and low cognitive workload across four validated instruments, with qualitative feedback highlighting transparency as critical for trust. Notably, users rated the system higher when no historical matches were available, suggesting on-device diagnosis provides value independent of knowledge base coverage. This pilot establishes safety and observability foundations for fleet-wide continuous improvement.

VIGIL: Towards Edge-Extended Agentic AI for Enterprise IT Support

Abstract

Enterprise IT support is constrained by heterogeneous devices, evolving policies, and long-tail failure modes that are difficult to resolve centrally. We present VIGIL, an edge-extended agentic AI system that deploys desktop-resident agents to perform situated diagnosis, retrieval over enterprise knowledge, and policy-governed remediation directly on user devices with explicit consent and end-to-end observability. In a 10-week pilot of VIGIL's operational loop on 100 resource-constrained endpoints, VIGIL reduces interaction rounds by 39%, achieves at least 4 times faster diagnosis, and supports self-service resolution in 82% of matched cases. Users report excellent usability, high trust, and low cognitive workload across four validated instruments, with qualitative feedback highlighting transparency as critical for trust. Notably, users rated the system higher when no historical matches were available, suggesting on-device diagnosis provides value independent of knowledge base coverage. This pilot establishes safety and observability foundations for fleet-wide continuous improvement.
Paper Structure (37 sections, 5 figures, 4 tables)

This paper contains 37 sections, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLM-enhanced IT operations.
  4. Agentic and tool-augmented systems.
  5. Edge and distributed AI.
  6. VIGIL Vision and Architecture
  7. Operational loop.
  8. Self-improvement loop.
  9. Agent Roles
  10. Planner Agent.
  11. Diagnosis Agent.
  12. Knowledge Agent.
  13. Remediation Agent.
  14. Implemented Edge System
  15. Evaluation
  16. ...and 22 more sections

Figures (5)

  • Figure 1: Operational and self-improvement loops within VIGIL. Device-level cognition, the focus of this paper, executes locally on endpoints (shown in blue), while cloud-level reflection, designed for future activation, refines policies, memory, and prompts over time (shown in red). Monitoring at the fleet level operates in parallel across both loops (shown in black).
  • Figure 2: VIGIL’s agentic architecture integrating local edge agents and remote cloud agents. Edge agents execute diagnosis and remediation on-device, while cloud agents provide coordination, governance, and observability across the fleet.
  • Figure 3: User experience survey: (a) SUS score distribution, (b) NASA-TLX workload, (c) user trust (bottom-left) and (d) technology acceptance
  • Figure 4: Normalized summary of the user experience survey across the four validated instruments (n=23)
  • Figure 5: Item-level SUS score breakdown (1--5 scale). Positive-framed items (higher is better) and negative-framed items (lower is better) are shown separately. The dashed line indicates the neutral midpoint (3.0).