Agentic systems—autonomous, goal-driven AI agents that act across services and user interfaces—are moving from experiments into production, creating new attack surfaces and governance challenges. As of 2026-04-01, enterprises require integrated approaches that combine infrastructure-level visibility, developer-facing agent builders, model acceleration, and runtime policy controls to manage risk across cloud and edge deployments. Key platform types include intelligent agentic infrastructure (e.g., Xilos, which positions itself as providing enterprise visibility into connected services and agent activity); no-code/low-code agent builders (Lindy, StackAI, Yellow.ai, IBM watsonx Assistant) that accelerate creation and orchestration of autonomous agents; model and inference clouds (Together AI) for fast training, fine-tuning, and scalable inference; and interface-focused agentic systems (Adept) that operate inside software interfaces. Conversational and developer assistants (Anthropic’s Claude family) are often embedded as agent brains or orchestrators. Security and edge protection priorities include end-to-end observability, credential and secrets management, policy enforcement at runtime, model provenance and fine-tuning controls, network segmentation for agent traffic, and hardened edge inference (attestation, secure enclaves, and lightweight policy guards). Combining visibility platforms with builder platforms and secure inference layers creates a defensible stack: infrastructure for monitoring and isolation, governance tooling to define policies, and model/runtime controls to limit unintended actions. This cross-cutting approach addresses the operational realities of agentic AI—rapid adoption, distributed execution, and emergent behavior—while enabling measurable governance and reduced risk in production deployments.