So many articles and blog posts you read today start with a sentence like “AI is coming and you had better be ready.” Then they go on to tell you that their product is going to save you from all of the headache and risk of adopting AI for your business. This isn’t one of those.
Well, sort of — because a) agentic AI is indeed showing signs of fundamentally reshaping the way we build and operate businesses online, and b) a truly scalable agent orchestration platform is a necessity to manage the chaos and risks that running agents at scale generates.
However, what I am not going to claim is that AI orchestration makes all the headaches go away. You still have to figure out when and how to create agents, and how to integrate them into workflows, (or long running tasks, or whatever). You still have to reimagine how you build your business in a world where you only need to describe a desired outcome to get a first stab at achieving that outcome.
But AI orchestration does enable you to deploy agent prompts to the models of your choice as they are needed, where they are needed, and with access to the tools and data they require. It makes your infrastructure (including data sources, graphic cards, and so on) align with model and agent needs. It also provides you with a library—or better yet, a garden—of applications, services, and agent templates that speeds up putting together a solution you can trust.
Why is that strategic? I think there are at least three arguments.
The Platform Argument
The first and most familiar way of exploring the strategic advantage of AI orchestration is very similar to the arguments used for other distributed system platform components, such as Kubernetes, Kafka, or even AWS. If you want to manage deployment and operations of distributed applications at scale, it is important to have predictable but flexible platform elements to do two things for developers and application operators: reduce toil and reduce risk.
To explain this, consider this well trodden analogy (pun intended).
Having every developer team hack their way through the wilderness of technical options and processes in order to deliver their respective applications or services leads to exponential growth in complexity for those teams providing infrastructure and service support. Each new technical option potentially conflicts with all the others that came before it, and thus adds significantly greater pain than “just one more thing to manage” for those responsible.
A consistent platform that “paves the path” for developer teams to quickly get from development to production not only greatly reduces that complexity, but it also leads to less cost and risk to the business. Advanced platform teams include things like information security, system readiness engineering, and cost optimization as “automatic” elements of their offerings. But even a very basic process removes the need for every development team to reinvent the wheel when it comes to production delivery.
AI orchestration removes the toil (and risk) associated with managing what AI models and agents need to run where, on what hardware, and with secure and efficient access to what data. It does this while remaining extendable, customizable, and capable of adapting to new agentic AI approaches.
The Data Value Argument
One fundamental thing about enterprise computing hasn’t changed in the age of AI: the most valuable resource an enterprise has is its data. Unique information and knowledge can only be gleaned from taking a comprehensive look across the organization’s portfolio.
However, moving data is expensive, and duplicating it in a variety of locations (and technologies) is prone to creating both temporal and factual errors in decision making. The ideal for any large organization is to maintain systems of record that can be trusted to represent the current state of their domains, and to drive insights by understanding both the semantic and collaborative relationships between the various forms of data across all domains.
AI orchestration should solve this issue by granting agents access to the right data at the right times across the enterprise. This requires much more than a traditional metadata store. What is needed is a “graph of graphs” designed to embed the digital knowledge of an enterprise regardless of data location, type or source.
This is a system that respects data gravity, but defies it by using inference to bring agents together with the data they need at any given moment.
The Hive/Swarm Argument
The final argument I will leave you with today is predicated on understanding where AI agentic systems are going architecturally. While it isn’t possible to declare a single standard architecture for agentic systems—and it may never be—there are an increasing number of examples of systems that demonstrate what could be.
Many of these examples are predicated on the idea that agentic systems will be, in some sense, self organizing. While the company will have control over the types of agents that may be created, the abilities and limitations of those agents, and the access each agent will have to company data, the formation of groups of agents to solve a specific problem will likely be done by other agents, or even some AI models themselves (e.g. Anthropic Claude’s sub-agents feature).
In this model, the concept of defining and deploying individual agents—and managing those agents for an extended period of time—goes away. Agents come and go as they are needed, and access to data becomes more defined by circumstance and role than by an ongoing connection.
To handle this future of computing, you need an orchestration platform that is capable of securely managing the location, security, and capabilities of agents. It needs to do this in a mixed use environment—one in which control over data access, the amalgamation of complex responses, and the optimization of infrastructure usage are primary functions. It’s not enough to have agents plugged into human defined workflows, you need to give your agentic environment the freedom to create agents, workflows, neural networks, or whatever comes next without having to redefine inference resolution or data access.
Why Be Strategic Now?
Finally, let me wrap up by pointing out that these strategic advantages aren’t tomorrow’s opportunity. Companies, government agencies, academic institutions, and new business ventures are already quickly adopting agentic AI in a variety of forms to meet unique needs. And while some early agentic AI efforts have stumbled1 — which is normal for highly disruptive technical trends—many are starting to find their footing2.
The successful projects are embracing partnerships with vendors that specialize in agentic AI technologies. And the difference in the speed of business growth for these companies is shocking. According to Forbes, some startups embracing agentic AI approaches have seen revenue grow from $0 - $20M in a single year.
This is, to be fair, a little bit of a chicken-and-egg problem. If you haven’t had successful agentic AI projects already, why do you need to scale your support for agentic AI? On the other hand, if agentic AI is going to be table stakes for business agility and execution in the foreseeable future, why wait to get those advantages today?
The good news is that there are ways to scale your agentic AI platform that gives you the flexibility to build and deploy agents across the enterprise while keeping the costs of doing so manageable.
1 https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/
2 Ibid - See section titled “What’s behind successful AI deployments?”
