Description
Agentic AI is moving from experimentation to production across factories, networks, and critical infrastructure. In regulated, asset-intensive environments, the primary risk is no longer model accuracy. It is loss of control, weak governance, and unclear accountability. The 2026 Agentic AI Decision Landscape is an independent, analyst-led framework designed to help enterprise leaders govern and scale Agentic AI across four critical architectural layers: ● Decision Ownership Layer – Systems that define autonomous intent and own operational outcomes. ● Domain-Specific Decision Systems – Industry-tailored decision logic for telecom, manufacturing, and energy infrastructure. ● Control, Governance & Defensibility (Core) – Non-negotiable capabilities for execution control, policy enforcement, human oversight, and signal integrity. ● Enablement Layer – Foundation models, agent frameworks, and data platforms that provide intelligence but do not govern decisions. This is not a vendor ranking or buying guide. It is a decision-centric architecture model built on publicly verifiable evidence to help CIOs, architects, and risk leaders align IT, OT, and security teams before procurement and large-scale deployment.