Agentic orchestration: Reliable, responsible, and visible agentic impact

AI agents are rapidly becoming table stakes for the enterprise. These AI-model-based entities are capable of advanced reasoning, decision making, and continuous learning. When AI agents are empowered to execute complex and dynamic processes end to end, we call this agentic automation. The hype is palpable—90% of U.S. IT executives believe they have business processes that can be improved by agentic AI.

However, agentic automation isn’t a one-person band. Agents need help to get access to the right business data and to produce accurate, reliable decisions. Think of agentic automation as an orchestra. It needs AI agents, automation robots, tools (including AI models), and people working together to deliver transformative outcomes. It’s no surprise that 87% of U.S. IT executives say interoperability between different technologies is important to success. Yet, every orchestra needs a conductor to make sure all the players are keeping time and following the music. With agentic orchestration now in public preview, this level of coordination is within reach.

In this article, I’ll explain why effective orchestration is so important for agentic workflows, and how it can be achieved.

AI agents: obstacles to value

Business leaders and transformation leads are eager to embrace AI agents and embed them into workflows. Indeed, Gartner predicts that agentic AI will make at least 15% of day-to-day work decisions by 2028.

However, the race to adopt and operationalize AI agents is heating up in a context of unprecedented process complexity. The average large company now uses over 230 enterprise applications, with disparate tools for collaboration, project management, and enterprise resource planning, just to name a few. Enterprises have become a complex patchwork of people, tools, and processes that are reliant on masses of unstructured data. This impacts efficiency as data silos form, employees struggle to find the right tools, and incompatible systems create time-consuming bottlenecks.

Business leaders need comprehensive data to make informed decisions and identify the most valuable commercial and improvement opportunities. However, they’re struggling to achieve this due to siloed data and a lack of visibility into their most important processes. A holistic, accurate understanding of their organizations remains a pipedream. This only makes it harder to track, monitor, and improve their change initiatives, restricting their ability to prove ROI and scale agentic AI.

Processes have never been more complex and data more fragmented. But rushing to deploy new AI agents won’t solve these problems. In fact, they could make the challenge worse, adding to the tech bloat and increasing costs while struggling to make a visible impact. Process owners and improvement leads also need to consider the associated data and security risks. There are valid concerns around the reliability of AI agents, which have the same tendency to hallucinate as other generative AI capabilities. Such mistakes are highly risky in enterprise workflows, especially in heavily regulated environments.