AI Edge & Agentic Security Platforms covers the tools and practices used to build, deploy, observe and govern autonomous AI agents — especially when they run at the edge or inside multi-agent workflows. As agentic systems move from experiments into production, organizations must balance latency, privacy and resilience requirements of edge deployments with the need for visibility, policy controls and model governance. Key components of the landscape include agent frameworks (LangChain’s engineering libraries and stateful primitives like LangGraph), no-code/low-code orchestration platforms (StackAI for enterprise agent build/deploy/govern), and observability/security infrastructure (Xilos’ claims of full visibility into connected services and agent activity). Foundation-model and production platform vendors such as Mistral AI emphasize efficient, open models plus enterprise production controls and privacy features. Enterprise assistant products like IBM watsonx Assistant provide no-code and developer-driven agent orchestration for business automation, while lightweight consumer and prototyping platforms (AgentGPT) and AI-native IDEs (Windsurf/Codeium, Cursor) embed agentic capabilities into developer workflows. Also relevant are emerging agent marketplaces that enable reuse and composability of agent skills, and edge vision platforms that push inference and agent decision loops onto devices to reduce latency and preserve data locality. By 2026 organizations are prioritizing runtime observability, least-privilege integrations, and governance controls across hybrid and edge deployments. Evaluations should therefore weigh developer ergonomics, governance features, runtime visibility, model provenance, and the operational constraints of edge hardware when selecting tools and platforms.