VIGIL: Towards Edge-Extended Agentic AI for Enterprise IT Support
arXiv:2603.16110v1 Announce Type: new 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 prov
arXiv:2603.16110v1 Announce Type: new 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.
Executive Summary
The article presents VIGIL, an edge-extended agentic AI system designed to enhance enterprise IT support. VIGIL deploys desktop-resident agents to perform situated diagnosis, knowledge retrieval, and policy-governed remediation directly on user devices. A 10-week pilot demonstrated significant reductions in interaction rounds and diagnosis time, with high user satisfaction and trust. The system's transparency and on-device diagnosis capabilities were particularly valued by users, even when historical matches were unavailable.
Key Points
- ▸ VIGIL is an edge-extended agentic AI system for enterprise IT support
- ▸ The system reduces interaction rounds by 39% and achieves at least 4 times faster diagnosis
- ▸ Users report excellent usability, high trust, and low cognitive workload
Merits
Effective Problem-Solving
VIGIL's ability to perform situated diagnosis and policy-governed remediation directly on user devices enables efficient and effective problem-solving.
Transparency and Trust
The system's transparency and explicit consent mechanisms foster high user trust and satisfaction.
Demerits
Scalability and Resource Constraints
The pilot was conducted on 100 resource-constrained endpoints, which may not be representative of larger, more complex enterprise environments.
Expert Commentary
The VIGIL system represents a significant advancement in edge-extended agentic AI for enterprise IT support. By leveraging on-device diagnosis and remediation, VIGIL can reduce the complexity and costs associated with traditional centralized IT support models. However, further research is needed to address scalability, data privacy, and security concerns. The system's transparency and trust mechanisms are particularly noteworthy, as they demonstrate the importance of user-centered design in AI-driven systems. As VIGIL and similar systems continue to evolve, it is essential to prioritize ongoing evaluation and refinement to ensure they meet the needs of diverse enterprise environments.
Recommendations
- ✓ Conduct further research on VIGIL's scalability and applicability to larger, more complex enterprise environments
- ✓ Develop and implement robust data privacy and security protocols to protect user data and ensure the integrity of VIGIL-like systems