Why Agent Orchestration Is the New Frontier for AI

For the past year, software companies have been exploring what AI agents can do, and many have made meaningful progress developing promising proofs of concept. Today, as the focus shifts toward scaling these pilots to production, the new question is who, or what, will manage AI agents once they start doing real work across enterprise systems.

The recent spate of product launches suggests that the enterprise AI market is shifting from building individual agents to operating entire fleets of them. For instance, IBM’s Think 2026 announcements talked about the need for an agentic control plane for orchestrating, monitoring, and governing agentic AI systems.

The shift is important because AI is no longer being pitched only as chat interfaces or coding assistants. Vendors are starting to position AI tools as systems that can coordinate tasks, call tools, and act on business data. However, while that promises to usher in an entirely new level of automation, it also creates a new software problem: companies need a control layer that lets them decide what agents are allowed to do, track what they have done, recover when they fail, and prove that their work meets business and compliance requirements.

Without that control layer, the risks of broader agentic AI adoption are enormous, potentially involving widespread systemic failures rather than isolated errors. After all, in a multiagent environment, where one agent depends on the outputs of another, just a single error, hallucination, or security breach can have a cascading effect.

Multiagent orchestration is the emerging concept of one coordinating agent breaking larger and more complex tasks into smaller jobs before assigning those jobs to specialist agents and then pulling the results back together. That way, no individual agent has access to systems and data it doesn’t explicitly need for its role or the autonomy to carry out any actions outside of its predefined scope. Of course, the same applies to managing human-led workflows, the key difference being that AI works much faster and can execute many more steps in parallel. That means that issues like permission errors, bad assumptions, or poorly governed handoffs can spread throughout the workflow before a human manager has the chance to intervene.

To counter these risks at the speed and scale, AI demands, enterprises need a control layer as part of what IBM describes as a broader “AI operating model.” At its annual Think conference on May 5, IBM announced the next generation of watsonx Orchestrate, a platform that gives enterprises a unified way to plan, build, deploy, and govern AI agents at scale. That’s significant, because many large organizations are already developing and deploying agents across teams, tools, and frameworks. Some are built internally, while others come from vendors or are embedded in applications. Either way, IBM argues that enterprises don’t necessarily need another way to build agents, but rather a way to operationalize those they already have.

For software companies, that means making agent orchestration central to their strategy, whether as part of their own product roadmap, via partner integration, or a layer controlled by a larger AI infrastructure vendor.