This topic covers platforms that combine on-device computer vision, agentic AI orchestration, and security operations capabilities to detect, investigate and remediate threats at the network edge. By 2026 organizations are deploying edge AI vision systems in cameras, industrial sensors and vehicles while also adopting autonomous SOC features—automated triage, runbook execution and lateral containment—to reduce mean time to detection and response. That trend raises new governance and regulatory needs: observability into agent actions, audit trails for model-driven decisions, vendor risk controls and industry-specific compliance (for example insurance and critical infrastructure). Key tool types and examples include: edge AI vision platforms that deliver low-latency inference and model lifecycle management; intelligent agentic AI infrastructure such as Xilos, which claims end-to-end visibility into connected services and agent activity; no-code/low-code agent builders like StackAI for rapid automation and governed deployment; domain-focused governance platforms such as Monitaur that centralize policy, monitoring and vendor governance for highly regulated sectors; and model and runtime providers—Mistral AI, Cohere and IBM watsonx—that supply private, customizable models, embeddings and agent orchestration primitives needed for on-prem or hybrid deployments. Practitioners evaluating Huskeys, Qevlar and competitors should weigh three priorities: edge performance and secure model lifecycle, run-time observability and agent governance, and controls for regulatory compliance and third-party risk. The market is maturing toward integrated stacks that pair lightweight on-device vision with enterprise-grade governance and auditability, enabling safer, more accountable autonomous SOC workflows at scale.