VIGIL: Towards Edge-Extended Agentic AI for Enterprise IT Support
🤖 AI Summary
This work addresses key challenges in enterprise IT support—namely, device heterogeneity, dynamically evolving policies, and the difficulty of resolving long-tail faults—by proposing an edge-resident intelligent agent architecture. Operating locally on end-user devices with explicit user consent, the agent performs context-aware diagnostics, retrieves enterprise knowledge, and executes policy-driven remediation, enabling autonomous decision-making without reliance on historical incident cases. The design ensures end-to-end observability and security for large-scale deployment. In a 10-week pilot involving 100 resource-constrained devices, the approach reduced user interaction rounds by 39%, accelerated diagnosis by over fourfold, and enabled self-resolution in 82% of matched cases. User feedback indicated high trust and low cognitive load. This study represents the first extension of AI agents to the edge for transparent, compliant IT operations.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Enterprise IT support
heterogeneous devices
evolving policies
long-tail failure modes
centralized resolution
Innovation
Methods, ideas, or system contributions that make the work stand out.
edge-extended agentic AI
situated diagnosis
policy-governed remediation
end-to-end observability
on-device intelligence
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