Agentic AI orchestration platforms coordinate multiple goal-driven AI agents, integrations, and human handoffs to automate complex enterprise workflows. This topic covers the tools and patterns enterprises use to build, test, deploy and govern agentic workflows—from low-code workflow builders to developer frameworks and infrastructure focused on observability and compliance. It is timely in 2026 because widespread adoption of multi-agent automation has shifted priorities from proof-of-concept demos to production concerns: interoperability with ServiceNow (ITSM), Coupa (procurement), and Experian (risk and identity), robust governance and auditability, resilience of stateful agents, and cost-effective infrastructure. Key categories include AI Automation Platforms and Low-Code Workflow Platforms (for citizen builders and cross-functional orchestration), AI Agent Marketplaces (discovering reusable agents and connectors), and Agent Frameworks (for engineering reliable, stateful agents). Representative tools illustrate these roles: Kore.ai emphasizes enterprise-grade multi-agent orchestration with governance and observability; LangChain supplies open-source frameworks and engineering tooling (including state management via LangGraph) for building, testing and deploying agents; GitHub Copilot accelerates developer productivity and helps implement autonomous workflows; Notion provides a block-based workspace to combine documentation, knowledge and automation around agent outputs; Google Gemini supplies multimodal model APIs for reasoning and synthesis; and Xilos offers visibility and infrastructure for agentic activity at scale. Enterprises should evaluate platforms by their connector ecosystem (ServiceNow, Coupa, Experian), end-to-end observability, governance controls (audit trails, access policies, data residency), and the balance between low-code orchestration and pro-code engineering. These considerations determine whether agentic automation can move from isolated pilots into reliable, auditable enterprise workflows.