Orchestration: The Missing Link in Enterprise AI

Enterprise adoption of generative AI (GenAI) is accelerating, but so is the risk of repeating old mistakes.

We’re seeing teams across industries deploy apps and agents to automate real work, such as summarizing release notes, forecasting budgets, onboarding employees, and reviewing contracts. These use cases can drive meaningful efficiencies on their own. But when deployed as standalone tools, they introduce a familiar problem: a growing tangle of disconnected micro-systems that are difficult to monitor, govern, and scale.

This is how AI silos form. Left unchecked, AI agent sprawl will quickly turn from an experiment to a liability—not a competitive advantage. The real solution isn’t building more agents; it’s orchestrating them. Orchestration is the backbone that allows agents to work together, share context, and scale safely—an essential enabler for enterprises that want AI agents to move beyond isolated use cases and perform meaningful, specialized work.

It’s Déjà Vu

We’ve seen this before. Enterprises have spent decades trying to rein in “content chaos”—data scattered across departments, platforms, and clouds. Entire industries evolved to address the issue, from enterprise content management to modern cloud data services.

Yet despite this progress, silos persist. As KMWorld recently pointed out, organizations still wrestle with overlapping systems and duplicated data that undermine efforts to modernize infrastructure and extract value from enterprise information. Now, we’re at risk of repeating this cycle—this time with AI agents.

In the rush to adopt GenAI, too many organizations are building agents in isolation, each tightly coupled to a specific use case, model, or department. Without orchestration, these agents remain siloed—unable to share memory, coordinate efforts, or adapt dynamically. Worse, they often require redundant integrations and oversight, recreating the same obstacles that enterprises have spent decades trying to escape: fragmentation, inefficiency, and a lack of operational coherence.

Why Orchestration Matters

True autonomous agents—those that plan, reason, and act independently—require far more than clever prompting. They need a platform-level orchestration layer that manages memory, coordinates workflows, ensures tool access, and preserves security.

Think of AI the way you think about finance: it’s not a single tool for a single team, but a system of interconnected tools, standards, and practices that must operate everywhere in the organization. In the same way financial processes are embedded across departments—guided by shared controls and common infrastructure—AI needs orchestration to ensure agents and applications work together, follow governance rules, and deliver value consistently at scale.

Without this foundation, organizations are stuck managing dozens of fragile workflows with no central visibility or control. If something breaks, troubleshooting becomes a game of whack-a-mole. Security risks multiply. Governance erodes. And the promise of AI—scaling intelligence across the enterprise—quickly gives way to complexity, chaos, and spiraling costs.

Using a platform to orchestrate AI provides the backbone of an agentic enterprise by acting as the connective tissue between agents, systems, and data. A strong orchestration platform enables:

• Memory and state management for long-running workflows;
• Task decomposition and parallelization, so agents can work together efficiently;
• Fine-grained access control based on business rules, user roles, or departmental policies;
• Dynamic model selection, routing tasks to the most appropriate LLM or tool based on content and context;
• Guardrails and observability, ensuring compliance and auditability across AI activity,
• And a standardized approach to quickly develop, deploy, and operate new agentic services.

Put simply, a platform approach to orchestration turns individual agents into a cohesive, intelligent system. This involves more than just chaining prompts or calling APIs. It’s about building the infrastructure for agents to operate reliably, securely, and intelligently at scale. But orchestration is only one piece of the puzzle. Becoming an agentic enterprise requires more than a strong technical foundation; it demands that AI be treated as an organization-wide capability, not just one-off IT projects. Business and technical teams need shared goals, clear accountability, and aligned priorities to ensure agents are solving the right problems and scaling in the right ways, a level of coordination that’s difficult, if not impractical, to replicate with homegrown systems. Gartner predicts that by 2028, 70% of organizations developing multi-LLM applications and agents will adopt integration platforms for orchestration and data connectivity.

Agent vs. Agentic: A Crucial Distinction

To be clear, organizations can still gain value from deploying standalone agents. Automating discrete tasks like policy summarization or customer intake can deliver meaningful impact. But building an agent is not the same as becoming an agentic enterprise.

Just as there’s a difference between using analytics and being a data-driven organization, there’s a distinct difference between adopting agents and designing agentic infrastructure. 

An agentic organization treats AI as a core capability woven throughout workflows, governed centrally, and capable of scaling intelligence across departments. It doesn’t rely on point solutions. It builds coordinated systems of agents that can reason, adapt, and work together. And it does this through platform orchestration.

The enterprise doesn’t just need one agent; it needs a network of agents that can coordinate with each other, access the right tools, data, and content, and execute complex, multi-step workflows across systems and departments. 

The Road Ahead

There’s a growing divide between companies dabbling in agents and those investing in agentic infrastructure.

The former will automate tasks. The latter will unlock compounding value by coordinating intelligence across teams, maintaining control over how agents behave, and adapting rapidly to change.

The companies that succeed in this next chapter of AI won’t just be the ones with the most agents. They’ll be the ones that know how to orchestrate them securely, intelligently, and at scale, while embedding AI as an organization-wide capability, not just a one-off or IT department project.

Agentic AI represents a fundamental shift, from building clever automations to designing systems that think, act, and adapt alongside your teams. But without a platform to orchestrate, even the best AI initiatives risk becoming the next generation of technical debt.

Agents are already here. The future belongs to those who can make them work—together.

Disclosure: GPT-4o was used to polish the final draft of this post, i.e., check spelling and grammar, smooth sentence flow, suggest alternative headlines or phrasing, etc. 

Chris McLaughlin is Chief Revenue Officer at Vertesia, where he leads the company’s global go-to-market strategy and helps customers build and operate GenAI solutions. He brings more than 25 years of experience in enterprise software, with leadership roles spanning high-growth startups and large global organizations.