Every major technology vendor is embedding intelligent agents into their platforms. From Salesforce to ServiceNow, Microsoft to AWS, enterprises are seeing a surge in AI-driven copilots, assistants, and automation bots. While these tools offer valuable localized intelligence, they also introduce complexity and fragmentation.
CIOs face a crucial question: How can we harness the benefits of AI agents without introducing chaos?”
A strategic framework for orchestrating AI agents at an enterprise scale is necessary. As every major platform embeds intelligent agents, CIOs must address how to coordinate, govern, and scale them across a fragmented landscape. Orchestration, more so than individual agent design, is the key to delivering consistent, enterprise-wide intelligence.
The emerging enterprise agent ecosystem
As enterprises increasingly adopt AI, a new agentic architecture is taking shape. This architecture separates the execution of intelligent tasks from the orchestration of workflows. Understanding this shift requires distinguishing between the types of agents deployed and the platforms that coordinate them.
Types of agents and where they operate
Application-bound agents
- Embedded within enterprise application suites such as Workday, Oracle, and Salesforce
- Deliver context-aware intelligence that is tightly integrated into the workflows, data models, and security frameworks of the host application
- Effective for specific use cases, but operate in silos and are limited to their native environment
Application-specific, externally-hosted agents
- Designed to support a particular application or domain, like payroll in Oracle or case management in Salesforce, but are built and run outside the application itself
- May be hosted on platforms like ServiceNow, Azure, or a custom orchestration layer
- Allows for more flexible control, broader integration, or adherence to enterprise architecture standards, while still focusing on a single system
Cross-application agents
- Designed to operate across multiple systems
- Frequently developed using platforms such as Azure, AWS, Google Cloud, ServiceNow, or UiPath
- General purpose, configurable, and capable of interacting with APIs, tools, and data sources beyond any single application
- Often serve as building blocks for broader automation and decisioning strategies
How agents are orchestrated
Vertical orchestration
Agents are managed internally within a platform such as Workday, Oracle, or Salesforce. These systems coordinate tasks within their own workflows, data models, and rules. Orchestration is tightly integrated but confined to the host application.Horizontal orchestration
Agents can be coordinated across systems. It includes two subtypes:Hyperscaler platforms
Providers such as Azure, AWS, and Google offer orchestration tools within their cloud ecosystems. These support multi-agent workflows but often assume use of native infrastructure and services.Neutral platforms
Platforms like UiPath, ServiceNow, MuleSoft, and Boomi offer vendor-agnostic orchestration. They are designed to integrate across diverse systems, making them well-suited for complex enterprise environments.
Convergence of roles
The boundaries between agent providers and orchestrators are beginning to blur. Some platforms that initially focused on embedding agents within their own environments are evolving to coordinate workflows across multiple systems. This shift reflects a broader trend toward combining vertical control with horizontal extensibility, enabling unified management of both native and external agents.
CIO imperative: Architect intelligence, not complexity
As intelligent tools multiply, the role of IT must evolve from operating and perfecting individual agents to designing systems that enable agents to work together safely and effectively. The priority, then, is to establish an AI orchestration layer that can coordinate distributed agents and apply enterprise policies that account for responsibility, governance, and optimization.
Strategic design: Principles for enterprise AI orchestration
The following principles are designed to help IT leaders decide where intelligence should live, how it should be coordinated, and what should be governed centrally.
1. Anchor agents where the data lives
Deploy agents in the systems that own the data or the system-specific business process they support. Application-based agents are often best suited for tasks that require native context, platform-specific workflows, or user experience continuity. Use them for automation within a single application, not across them.
2. Use centralized intelligence for cross-system logic
When workflows or decisions span multiple platforms, orchestration must shift to a centralized layer. Hyperscaler or neutral orchestration platforms can manage logic across applications using LLM-native tools like memory, retrieval, and function chaining. This is where horizontal intelligence belongs.
3. Architect for change, not just control
The AI landscape is evolving rapidly. Prioritize agent orchestration models that are modular and interoperable. Favor open APIs, loosely coupled systems, and abstraction layers that allow you to adapt without starting over.
4. Govern where you have control
Not all platforms offer equal visibility or control. Ensure agent orchestration happens in environments where you can apply enterprise-wide policies and monitor behavior. Build governance into the orchestration layer from the start, with clear policies, escalation paths, and lifecycle management.
5. Design for the user’s work surface
Agents should meet users where they are, whether in Teams, Slack, ServiceNow, or other frontline tools.
As agentic systems mature, they are evolving into the primary interface through which users interact with enterprise systems. This flips the traditional UX paradigm - from navigating multiple applications and forms to simply stating intent (e.g. “Start onboarding for Jane Doe”) and letting the agent handle the workflow.
In this model, UX is no longer defined by screens and clicks, but by intent to conversation to action. Intelligence should be surfaced at the point of interaction, not just where the logic resides. A seamless user experience is essential to adoption and trust.
Example in practice: Coordinating an employee onboarding workflow
Consider a common enterprise scenario: employee onboarding. HR manages hiring in Workday, IT handles equipment provisioning in ServiceNow, and identity creation is handled through Azure Active Directory. Each platform now includes its own embedded agent, each capable of automating part of the process. However, if these agents operate in isolation, the result is fragmented automation, duplicated logic, and limited visibility across the workflow.
In this employee onboarding scenario, a viable solution is to orchestrate in ServiceNow, which benefits from being a platform for both vertical and horizontal orchestration.
To address this, organizations need a unified orchestration layer that can coordinate actions across systems, trigger workflows through APIs, and deliver a consistent user experience.
This onboarding example brings several key principles into focus:
- Anchor intelligence where the source data resides, such as Workday for HR-specific processes
- Centralize logic when it spans multiple systems
- Surface workflows in the tools employees already use
- Choose orchestration models that can evolve as the environment changes
A well-designed orchestration layer transforms a collection of isolated tools into a unified system capable of delivering consistent, context-aware intelligence at scale. As we enter the next era of enterprise AI, success will be defined by the strength of an organization’s connective architecture.
Reach out for help orchestrating across your AI agent ecosystem.
Other critical lenses of agent orchestration
In addition to an enterprise's AI agent ecosystem, leaders must consider two other critical lenses of agent orchestration: Workforce design and technical architecture.
Workforce design
Agent orchestration is redefining how work gets done. As AI agents integrate into core operations, they challenge traditional views of roles and responsibilities. This prompts a fundamental shift in process design, workforce planning, and governance. These digital workers are not simply automating tasks; they are becoming collaborative participants in hybrid human-agent teams.
Learn moreTechnical architecture
Agent orchestration introduces architectural requirements that extend beyond traditional modularity and API integration. The key shift lies in enabling runtime decision-making where agents interpret context, adapt plans, and coordinate with others in real time. Modern systems emphasize interoperability, but agentic architectures must support intelligent, autonomous collaboration between agents and humans during execution, not just at design time.
Architecting for this model demands rethinking data flow, observability, exception handling, and trust mechanisms. These elements are also foundational to responsible AI, ensuring that agentic systems remain transparent, auditable, and aligned with organizational values and regulatory requirements.