A practical guide to agentic AI and agent orchestration

Artificial intelligence is evolving from task-specific tools to systems that can manage entire workflows. We already automate complex processes across industries, such as:

• Discharge planning in healthcare
• Grants management in higher education
• Financial close in finance

These solutions are often rule-based, relying on predefined logic and structured pathways. But rules are not always available or obvious, and in these cases, we resort to our innate ability to complete a task using reason. Agentic AI represents the next step.

Agentic AI enables systems to perform human-like reasoning to determine what to do to complete a task. It includes capabilities that allow systems to interpret context, adapt plans in real time, and coordinate across functions and platforms. This evolution builds directly on the foundations of automation, enabling a shift from static process automation to dynamic, end-to-end workflow management.

The impact is significant. Organizations that embrace agentic AI can reduce manual coordination, accelerate cycle times, and improve decision quality at scale. But capturing this value requires leaders to rethink how work is done and how systems are designed.

Optimizing individual applications or hardcoding integrations is no longer sufficient. Instead, systems must be built for interoperability, context awareness, and real-time orchestration. Success depends on aligning AI capabilities with business priorities, data architecture, and governance; most importantly, we must ensure humans and machines can operate cohesively across workflows.

What are AI agents?

AI agents are specialized software entities designed to autonomously achieve defined goals by continuously observing data, assessing options through logical reasoning, and executing actions independently or with minimal human intervention. Examples include virtual customer service agents that handle complex inquiries around the clock, digital procurement assistants that autonomously manage vendor interactions, and healthcare agents capable of drafting preliminary clinical appeal letters based on contextual analysis.

What is agentic AI?

Agentic AI refers to a system-level capability that enables software entities to pursue broader objectives through long-horizon planning, contextual decision-making, and dynamic coordination of multiple tasks or agents.

Unlike task-specific AI agents, agentic AI can understand the bigger picture, adapt to changing conditions in real time, and orchestrate actions across functions, systems, and stakeholders.

Examples include:

• AI-driven hospital discharge managers that coordinate across care teams, pharmacy, and transport based on real-time status
• Financial close assistants that manage dependencies across multiple entities and systems to streamline month-end processes

How do AI agents and agentic AI relate?

AI agents are functional building blocks, each designed to perform a specific task with autonomy. Agentic AI brings these building blocks together, adding the ability to reason across tasks, manage priorities, and orchestrate them in service of a broader goal.

In essence, AI agents execute. Agentic AI governs and coordinates, enabling intelligent end-to-end workflow management.

What is agent orchestration?

Agent orchestration is the coordinated management of multiple AI agents or digital systems to achieve complex goals more efficiently and intelligently. Rather than relying on a single solution to handle every task, agent orchestration brings together specialized agents with distinct capabilities to collaborate, adapt, and solve challenges in real time.

By integrating decision making, communication, and task execution across systems, organizations can scale their operations, personalize experiences, and drive faster outcomes. As AI continues to evolve, and agents become more prolific, orchestrating multiple agents effectively will be critical to building flexible, resilient technology ecosystems that align to enterprise goals.

Why now? Technology and market forces are converging.

Accelerated adoption of agent orchestration stems from a convergence of technological advancements and economic necessities. Key developments include:

• Generative AI breakthroughs: Generative AI (GenAI) innovations, like ChatGPT and similar models, have significantly advanced AI capabilities, lowering the barrier to implementing sophisticated agent systems.
• Composable architectures: Organizations are increasingly adopting modular, composable architectures, enabling rapid deployment, testing, and scaling of AI agents without extensive overhauls.
• Hyperscaler toolkits: Major cloud providers (AWS, Microsoft Azure, Google Cloud) now offer comprehensive, easy-to-implement toolkits that reduce the technical and financial barriers to agent orchestration.

These advancements have democratized access to sophisticated AI capabilities, making scalable, enterprise-level orchestration achievable across industries.

As organizations increasingly integrate AI, orchestration emerges as a strategic imperative rather than an optional component. It determines whether investments result in sustainable growth and innovation or become isolated experiments with limited impact.

To facilitate effective orchestration, leaders must consider three interconnected perspectives: their workforce design, their solutions ecosystem, and their technical architecture.

Start a conversation with one of Huron’s AI experts to discuss agent orchestration in your environment.

Digital agents are rapidly becoming crucial workforce components, already demonstrating measurable value across industries. AI agents currently approve low-risk invoices, facilitate overnight customer onboarding, and, according to Gartner, are predicted to autonomously resolve approximately 80% of common customer service issues without human intervention.

As the technology evolves, agents will take on increasingly complex and nuanced work. Organizations must view these agents as integral teammates, rather than mere technological add-ons, to unlock significant gains in capacity, compliance, and operational speed.

Agents are essential workforce units

Practical implementations clearly demonstrate the growing role of agents within workforce strategies:

• Finance and procurement: Agents autonomously manage invoice pre-approvals, significantly reducing processing times and error rates.
• Customer operations: Digital agents conduct seamless overnight customer onboarding and compliance document collection, ensuring faster customer activation and improved satisfaction.
• IT services: AI-driven triage agents independently resolve nearly half of all IT tickets, substantially reducing resolution times and freeing IT personnel for more complex tasks.

By embedding agents deeply into operational processes, organizations enhance efficiency and redeploy human talent to higher-value strategic activities.

Operating models must change in the wake of agent-orchestrated work

Effective integration of digital agents into the workforce requires organizations to rethink traditional operating models: roles and responsibilities, governance and compliance, processes, metrics, and talent/culture.

Roles and responsibilities

To lead in this new paradigm, organizations must move beyond a binary view of “human versus agent” and instead evaluate tasks across a spectrum:

• Which tasks can be orchestrated entirely by agents to unlock capacity and speed?
• What tasks benefit from human-AI collaboration or augmentation?
• What should remain the domain of human judgment, empathy, or creativity?

These considerations ensure clear accountability and optimized resource allocation. Tasks suitable for agent delegation typically feature high repetition, clearly defined rules, significant data intensity, and minimal empathy or subjective decision-making requirements. Human roles focus increasingly on strategic oversight, complex problem-solving, empathy-driven interactions, and nuanced judgment.

Governance and compliance

The integration of agents necessitates robust governance structures, including:

• Explicit human-in-the-loop policies
• Comprehensive auditable activity logs
• Clearly defined board-level oversight protocols

These governance practices mitigate risks, address regulatory concerns, and build stakeholder trust by ensuring transparency and accountability in agent-driven decisions.

Processes need to be re-imagined

Think of agents as one of the swim lanes in your business process designs. Successfully embedding agents into workflows requires organizations to systematically reimagine processes using structured methodologies:

• Knowledge curation: Build structured, accessible knowledge bases and datasets that agents can reference to act reliably and accurately.
• Workflow decomposition: Break down processes into tasks to identify potential entry points for agents.
• Task scoring: Evaluate each task based on repetition frequency, associated risk level, data processing requirements, and necessity for human empathy.
• Allocation strategy: Clearly and deliberately assign tasks to agents, humans, or hybrid teams, explicitly detailing compliance checkpoints and control measures.

This rigorous methodology helps organizations maximize efficiency and compliance while delineating responsibilities across human and digital workers.

Workforce strategies must shift, introducing new KPIs

The advent of agent-orchestrated work demands a recalibration of workforce management and performance measurement approaches:

• Capacity hours released: Quantify the workforce capacity freed by agents handling repetitive tasks, providing clear insight into productivity gains.
• Decision latency: Measure and monitor the time reduction caused by agent assistance in reaching operational decisions or process completions.
• Escalation rate: Track the frequency and types of tasks escalated from agents to human counterparts, providing critical insights into agent effectiveness and areas for improvement.

Additionally, organizations must prioritize upskilling human employees in areas such as agent prompt design, agent oversight, and collaborative problem-solving.

The role and function of AI agents will continue to evolve, so organizations should foster a culture of work agility and continuous learning over the long term. This ensures human teams effectively partner with AI agents, maximizing organizational efficiency and innovation.

Human workers need to be prepared for agent-orchestrated teams

Successful integration of digital agents hinges on technical implementation, but more importantly, on robust human-centric change-management strategies. Employees should be provided with clear communication, targeted training, and continuous support to comfortably adapt to collaborative interactions with AI teammates.

Actively involving employees in AI implementation creates a sense of ownership that increases adoption rates while allowing organizations to benefit from front-line insights that lead to more effective customization.

Transparent discussions on agent capabilities, limitations, and expected workflows alleviate fears and build confidence, paving the way for smoother transitions and increased adoption.

Embrace the hybrid workforce model

Hybrid human-agent teams represent the future workforce model. Organizations that can successfully blend human intelligence with AI agent capabilities gain competitive advantages through improved operational efficiency, enhanced employee engagement, and superior customer experiences.

Digital agents become active teammates, with clearly defined roles, metrics, onboarding processes, and career-development pathways aligned similarly to human employees. Implementing and sustaining this type of model is a team effort and requires the partnership of functional experts, IT, and human resources at each step.

Huron’s AI experts can help your organization develop a hybrid workforce model that successfully blends human intelligence with AI agents. Reach out to learn more.

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.

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.