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.
Three critical lenses of agent orchestration
Effective orchestration must consider three interconnected perspectives: workforce design, solutions ecosystem, 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.
Solutions ecosystem
Today's market offers diverse platform options for agent orchestration, each with distinct advantages and trade-offs in terms of speed, flexibility, and control.
- SaaS application vendors: Oracle, Workday, and Salesforce are embedding AI agents directly into their platforms.
- Cross-application orchestration platforms: UiPath and ServiceNow provide greater interoperability.
- Hyperscaler frameworks: Microsoft Azure, AWS, and Google Cloud offer the highest degree of customization.
- Citizen development tools: Microsoft Copilot Studio and ChatGPT’s custom GPTs allow business users to build lightweight, task-level agents or automations without code.
Strategic decisions about orchestration platforms must account for more than just technical fit. Leaders should assess trade-offs in scalability, time to value, interoperability, vendor lock-in, and operational risk. The right mix of platforms will depend on an organization’s maturity, integration needs, and governance posture.
Technical 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.
Orchestration as the path to sustainable AI value
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.
Organizations that act early to define their approach to agent orchestration will be best positioned to lead in efficiency, agility, and innovation.