Top AI Agent Examples and Industry Use Cases

In this article we discuss:

• What Makes AI Agents “Agentic?”
• AI Agent Use Cases by Role and Industry
• How to Select AI Agent Use Cases
• What Agentic AI Means for the Human Workforce
• What’s Next for AI Agents?

Artificial intelligence (AI) has already become embedded into the core of enterprise operations, automating tasks and providing data-driven insights at scale. But a new, even more sophisticated wave of AI is taking hold in 2025 that takes enterprise AI to a new level. AI agents—autonomous systems that can reason and act with limited human intervention—are handling workflows that once relied entirely on human judgement.

As organizations grapple with rising complexity, constrained resources, and relentless change, AI agents offer something rare: scalable intelligence that acts with purpose. And this evolution is underway across industries; Gartner predicts 33% of enterprise software applications will include agentic AI by 2028—up from just 1% in 2024—and at least 15% of business decisions will be made autonomously via agents.

From finance to healthcare to retail and more, intelligent AI agents are fundamentally reshaping how teams operate, how decisions are made, and the customer experiences companies can provide.

What Makes AI Agents “Agentic?”

Agentic AI is defined by its capacity for purposeful, self-directed behavior. Agentic systems sense their environment, process signals, make informed decisions, and take action—all in pursuit of specific outcomes. What makes them distinct from other AI systems isn’t just what they do, but how they do it: continuously, contextually, and with limited dependence on human direction.

Several core capabilities make an AI system agentic:

• Autonomy: The ability to operate independently within defined boundaries, without constant human instruction.

• Goal orientation: Agents pursue clear outcomes, evaluating actions based on how well they advance those goals.

• Adaptability: They monitor real-time inputs and adjust decisions dynamically as context shifts.

• Reasoning: Agents assess trade-offs, apply rules or models, and choose actions that reflect logical prioritization.

• Learning: Some agents can refine their behavior over time by analyzing the outcomes of past decisions.

• Collaboration: In multi-agent systems, agents coordinate and share knowledge to solve problems collectively.

Together, these capabilities define what makes AI agentic: the ability to act independently, reason through trade-offs, adapt to change, and pursue outcomes with minimal human direction. But agentic AI behavior isn’t one-size-fits-all; it shows up in different ways depending on the agent’s design and objective.

Types of AI Agents

AI agents vary widely in complexity and capability, from rule-based systems that follow predefined triggers to adaptive agents that learn, reason, and collaborate. Each type is purpose-built for a specific class of tasks and operational goals. Below are the agent types most commonly embedded in enterprise workflows today.

Reactive Agents

Reactive agents, also known as simple reflex agents, respond to current inputs based on predefined rules. They’re ideal for automating repetitive, structured decisions, such as filtering spam emails or initiating a default process when a sensor threshold is crossed.

Model-Based Reflex Agents

Model-based reflex agents maintain an internal model of the world around them. This allows them to act even when not all information is visible—what’s known as a partially observable environment. For example, in warehouse robotics, they can infer the likely location of an item when some shelf data is missing by using their internal understanding of layout and past patterns.

Goal-Based AI Agents

Goal-based agents are designed to achieve specific goals by selecting actions that move them closer to a desired outcome. Unlike reflex agents that react to inputs moment by moment, goal-based agents evaluate the current state, consider possible future states, and choose the best path toward their objective. For example, in a logistics scenario, a goal-based agent might reroute deliveries based on traffic and weather conditions to ensure on-time arrival.

Utility-Based Agents

Utility-based agents evaluate not just whether an action will meet a goal, but how valuable that outcome is relative to others. A utility-based agent, for instance, might prioritize fulfilling an order for a high-value client over a lower-value one when resources are limited. Their strength lies in making trade-offs that optimize business impact, not just task completion.

Learning Agents

Learning agents improve their performance over time by analyzing new data and feedback. Rather than relying solely on static rules, they adjust their behavior based on experience, enabling them to handle unfamiliar situations more effectively. For example, a fraud detection agent might start with historical patterns but learn to flag new forms of suspicious activity as it encounters evolving attack methods.

Collaborative Agents

Also known as multi-agent systems, collaborative agents coordinate with other agents—or with humans—to solve large-scale or interdependent problems. In supply chain management, for example, a network of collaborative agents can each monitor different logistics points (such as warehousing, shipping, inventory) and collectively adjust operations in real time to minimize bottlenecks.

Commerce AI Agents

Commerce AI agents are built for high-volume retail and e-commerce environments. They power dynamic pricing systems that can adjust pricing in real time based on current demand, competitor activity, and available inventory. For example, during a flash sale, a commerce agent might automatically raise prices for fast-selling items while discounting slower-moving stock to maximize revenue.

Customer Support Agents

AI customer support agents use natural language processing to resolve frequent inquiries without requiring human intervention. They can reset passwords, track orders, or handle refunds, freeing up live agents for more complex customer issues. As they interact with more users, these systems become smarter, faster, and better at deflecting volume while maintaining service quality.

AI Agent Use Cases by Role and Industry

Leading industries are putting AI agents to work in ways that go well beyond process automation. With each passing month, the real world examples of AI agents are increasing exponentially.

Agentic AI systems are helping organizations make strategic decisions, tackle complex tasks, improve resilience, and elevate experiences for employees and customers alike. Let's take a closer look at how AI agents are already driving meaningful progress in key sectors:

• AI Agents in HR
• AI Agents in Finance
• AI Agents in Healthcare and Life Sciences
• AI Agents in Higher Education
• AI Agents in Retail and Hospitality

AI Agent Examples in HR

Human resources is increasingly tasked with balancing operational efficiency and personalized employee support. AI agents are helping HR teams scale critical processes—like onboarding and internal mobility—while enhancing the employee experience.

• Virtual HR agents resolve common employee questions—benefits, leave policies, or pay issues—through conversational interfaces that reduce tickets and boost satisfaction.

• Internal mobility agents scan skill profiles, performance history, and open roles to recommend personalized career opportunities, surfacing options employees may not have considered.

• Onboarding agents coordinate personalized task lists, automate reminders, and adapt workflows based on role, region, and contract type, reducing friction from day one.

• Performance feedback agents analyze goals, feedback cycles, and team dynamics to prompt timely reviews and growth conversations.

• Skills inference agents identify emerging or latent capabilities across the workforce by analyzing project involvement, feedback, and behavioral data.

AI Agent Examples in Finance

Finance teams operate in a high-stakes environment that demands accuracy, speed, and control.

From daily transactions to strategic planning, the volume and complexity of financial data continues to rise—leaving little room for inefficiency. AI agents are stepping in to help teams move faster while staying compliant and precise.

• Journal insights agents proactively flag anomalies in transactions before the close process begins. These agents operate continuously and help finance teams investigate issues early, reducing last-minute errors and delays.

• Forecasting agents synthesize financial, operational, and external data to update forecasts autonomously. They identify outliers and suggest revised projections, helping decision-makers course-correct faster.

• Expense monitoring agents track trends across departments, flag policy violations, and surface unusual spending behavior in real time—ensuring tighter compliance and accountability.

• Variance analysis agents investigate deviations between actuals and forecasts, providing context and surfacing potential causes without manual data stitching.

• Liquidity management agents model short-term cash flow scenarios using real-time inputs, giving finance teams early warnings and options for mitigation.

AI Agent Examples in Healthcare and Life Sciences

In healthcare and life sciences, operations must meet the dual challenge of compliance and compassion. AI agents offer new ways to reduce administrative burden, improve scheduling, and ensure the accuracy of credentialing and audit trails while maintaining human focus on what matters most: Patient outcomes.

• Credentialing agents continuously validate licenses and certifications against system records and external databases, notifying managers of lapses before they create risk.

• Workforce scheduling agents balance patient load, staff qualifications, union rules, and preferences to generate optimized shift plans in minutes.

• Audit preparation agents automatically tag and categorize documentation to ensure teams are ready for audits without manual file searches.

• Inventory agents track medical supply levels and reorder thresholds, ensuring availability without overstocking.

• Patient intake agents streamline onboarding by automating data collection and pre-visit screening for routine care.

AI Agent Examples in Higher Education

Higher education institutions face growing complexity across enrollment, curriculum management, and student services. AI agents help colleges and universities operate more efficiently, delivering a better experience for students and faculty.

• Student support agents provide 24/7 answers on financial aid, registration, or housing, while reducing queue times and freeing up staff capacity.

• Faculty planning agents recommend schedules and course loads based on faculty availability, qualifications, and departmental goals.

• Research grant agents track spending against requirements and deadlines, helping institutions stay compliant while funding innovation.

• Curriculum alignment agents map learning objectives to course offerings, helping academic leaders close skill gaps and strengthen program relevance.

• Retention agents analyze behavioral and academic data to flag at-risk students early and suggest targeted interventions.

AI Agent Examples in Retail and Hospitality

In service-focused industries like retail and hospitality, responsiveness and efficiency are vital. AI agents help frontline teams manage everything from staffing to supply chain in real time, while maintaining a strong customer experience.

• Scheduling agents act as virtual assistants and dynamically adjust rosters in response to foot traffic, sales velocity, or last-minute changes in availability.

• Supply chain agents monitor inventory levels and trigger reorders before stockouts occur, factoring in demand forecasts and vendor lead times.

• Employee experience (EX) agents flag burnout risks and disengagement signals using behavioral data—then suggest interventions.

• Customer service agents provide instant support for common requests, freeing up staff to focus on complex interactions.

• Pricing optimization agents analyze market trends, customer behavior, and stock positions to adjust pricing strategies in real time.

How to Select Agentic AI Use Cases

While agentic AI systems are increasingly powerful, their impact depends on how thoughtfully they are applied. Choosing the right use cases is one of the most important steps in any agent deployment strategy.

Identifying the right AI agent functionality for your business is not just about what can be automated—it’s about where agents can deliver meaningful business value with clarity and control.

The best use cases tend to share three characteristics:

• Clear goals that agents can pursue autonomously

• Access to clean, structured data

• Repeatable logic that guides decisions or actions

To help prioritize, leaders can use a simple matrix that maps potential use cases by their strategic value and readiness for automation:

• High value, high readiness: These are prime candidates. They include workflows like variance analysis, routine employee support, and credential validation.

• High value, low readiness: Worth investing in, but may require additional process clarity or data cleanup first.

• Low value, high readiness: Often useful for testing or early wins, but may not scale impactfully.

• Low value, low readiness: Typically better to avoid.

This approach helps organizations stay focused and avoid overengineering for use cases that are too complex or too marginal. Starting with high-impact, high-readiness workflows builds momentum, confidence, and clarity.

Choosing the right use cases also means aligning stakeholders around success metrics. That might mean reduced cycle time in finance, faster resolution in HR, or improved compliance in healthcare. With these metrics in place, organizations can scale with purpose—and make agentic AI a sustained part of how business gets done.

What Agentic AI Means for the Human Workforce

It may seem that agents are poised to replace human employees in many areas but agents (and other AI technologies) actually bring new opportunities for employees to be more strategic, innovative, and impactful.

According to the Workday research, 83% of workers believe that AI can boost their careers by helping them develop new skills in their roles and focus on more meaningful work. These findings reflect a growing shift: AI agents are not just tools of efficiency, but enablers of deeper human contribution.

Rather than displace jobs, agentic AI changes the nature of work. Agents take on repeatable, decision-bound tasks that follow clear logic or structured workflows. In doing so, they free up people to focus on work centered on creativity, intuition, innovation, and the ability to adapt amidst change and uncertainty.

This shift raises new requirements for how organizations design work. 

Human workers need access to transparent agent behavior, opportunities to intervene, and training to develop new digital fluency. Organizations, in turn, must invest in skills strategies that reflect the changing nature of work, not just the technologies driving it.

Workday research highlights that the most valuable business skills are not narrowly technical, but human. Relationship building, emotional intelligence, ethical decision making, and adaptability rank among the top capabilities believed to be irreplaceable by new AI technologies. These are not soft skills—they are durable ones, even as AI agents take on a bigger role.

As agentic AI becomes a more embedded part of how business operates, the question is not whether human work will remain relevant. It’s how organizations will empower it with AI to grow its impact and value. 

What’s Next for AI Agents?

The future of agentic AI is not a linear extension of what we see today. AI agents will introduce new frameworks for enterprise systems, workforce design, and decision-making models. The most important developments to watch include:

• Multi-agent ecosystems: AI agents will begin to operate collaboratively across domains and functions. Rather than work in isolation, agents will coordinate actions and handoffs—accelerating enterprise responsiveness while introducing new challenges for orchestration.

• Continuous, agent-led workflows: Traditional cycles like annual planning or quarterly reviews will give way to dynamic processes guided by real-time signals powered by machine learning. Agents will drive continuous forecasting, scenario testing, and course corrections across business functions. 

• Decentralized governance models: As agents make more decisions autonomously, organizations will need distributed oversight frameworks. Success will depend on visibility into agent actions, clear escalation rules, and collaborative governance shared across business and technology teams.

• System and infrastructure realignment: Agentic workflows will require interoperability across systems of record, planning platforms, and workflow engines. Technical architectures will evolve to prioritize composability, observability, and shared context.

• Workforce transformation as a design priority: Businesses will need to align skill development, team structures, and performance measurement with the new capabilities agents introduce. This means investing in human-AI collaboration readiness alongside technical deployment.

The role of agentic AI is expanding—and so is the scope of decisions that must be made around it. Leaders must be intentional when shaping that transformation to unlock long-term advantage.

Want to go deeper on how agentic AI is transforming work at scale? Discover how Workday is building enterprise-grade AI agents designed for trust, impact, and action.