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.