AI agent use cases 

Unlike previous types of AI tools—assistants, chatbots—which operate on a single-task basis, agentic AI systems can autonomously plan, reason and run complex tasks with minimal human intervention.

This makes AI agents uniquely capable of changing the very nature of day-to-day work. As Arvind Krishna, Chairman and CEO of IBM, recently said at IBM Think: “The enterprises pulling ahead are not deploying more AI—they’re redesigning how their businesses operate.”

The distinct ability of agentic AI to call on external tools to complete complicated directives is reshaping how enterprises approach automation. AI agents can also collaborate with other agents and technologies, a capability widely heralded as a catalyst for realizing the full potential of AI across the business landscape.

Leading businesses have begun to integrate AI agents and agentic systems into everyday real-world operations. According to recent research from the IBM Institute for Business Value, 80% of executives are increasing investment in agentic AI, with spending projected to nearly triple by 2027. Many of these investments will prioritize complex, multi-agent systems. According to the same research, 75% of business leaders anticipate that different agents will handle different problems, producing richer solutions as a result.

These artificial intelligence‑powered “digital workers” can be effective in streamlining customer support, optimizing supply chains, supporting human agents in marketing and sales departments and improving the employee experience. They also analyze data from the financial and healthcare industries. They deliver these benefits across multiple functions, helping organizations enhance efficiency and decision‑making. 

AI agents autonomously perform tasks, designing workflows by using available tools.

Agentic AI is based primarily on large language models (LLMs). Where traditional LLMs produced outputs based solely on the data used to train them and possessed limited reasoning abilities, AI agents can call on more tools and APIs to meet difficult goals. This shift enables them to operate beyond the constraints of their training data.

This shift enables them to operate beyond the constraints of their training data and handle increasingly complex tasks. Agentic AI can autonomously obtain current data, optimize workflows and create subtasks based on its objectives.

With advancements in gen AI and conversational AI technology, some agents interact with their human counterparts in natural language. And unlike previous LLMs or chatbots, AI agents store memory from one interaction to the other, improving reasoning power and accuracy over time.

Generally, AI agents are most useful when developed as part of a network. There are five central types of AI agents with varying levels of complexity. They are: 

• Simple reflex agents, which perform based on a single set of rules. They do not hold memory or query other agents when they’re missing information.

• Model-based reflex agents, which complete specific tasks based on a single set of rules but retains memory. A model-based reflex agent updates its model as it receives new information.

• Goal-based agents. which call on external tools to plan and execute a predefined specific goal.

• Utility-based agents, which call on external tools to select a series of actions to reach a goal and a predefined utility for that goal, such as a time requirement.

• Learning agents, which possess similar capabilities to other types of agents but have a unique capacity to learn. New inputs are continuously added to their knowledge base autonomously.

AI agents are used across industries to perform tasks autonomously, optimizing existing workflows and reducing manual work. 

In agriculture, AI agents help farmers increase yield while reducing waste.

The technology is capable of independently monitoring weather forecasts and soil conditions to optimize planting schedules and soil conditions. By continuously learning from environmental data and other inputs, AI agents help farmers make sustainable and cost-effective decisions to improve productivity.

In banking and financial services, agentic AI can be used to improve decision-making, optimize workflows and enhance compliance.

For instance, autonomous AI is used to perform continuous, autonomous fraud detection to detect unusual patterns and respond to emerging threats. Using similar logic, it’s well positioned to assist with compliance monitoring and loan underwriting, both of which involve a high volume of data-intensive repetitive tasks.

According to a recent World Economic Forum report, a suite of well-designed agentic AI pilots for financial institutions provided a raft of benefits. Together, they saved an estimated 30,000 workdays, and improved productivity on administrative tasks, documentation and preliminary analysis by 20%-59%. 

On the customer-facing side, AI agents and agentic virtual assistants provide AI-driven financial advisory services. For instance, they can automate certain wealth management activities or craft investment strategies based on market conditions and individual risk tolerance. By using AI solutions for financial management, enterprises mitigate potential disruptions and use data to maximize value and increase operational efficiency.

In disaster scenarios, AI agents can provide real-time intelligence and decision-making support for first responders.

These systems analyze satellite imagery, sensor networks and social media to assess damage and prioritize emergency response efforts. Predictive models and simulations also help localities prepare for future events. Tools such as these can enable proactive evacuations and minimize casualties, saving lives and reducing disaster response costs.

In education, agentic AI tutors and learning platforms provide personalized and scalable learning paths for individual students. 

AI‑powered mentoring agents assess a student’s level of knowledge and track their progress. They then adapt content in real time. This adaptive‑learning capability helps ensure that all learners receive appropriately paced instruction.

These agents can independently generate exercises, give feedback and explain context when students struggle with certain concepts. They’re also useful in responding to and learning from students’ divergent learning styles.

In higher education, AI research assistants can help students explore topics by gathering sources or summarizing information.

In addition, language learning apps and career training platforms increasingly integrate autonomous agents that simulate real-world interactions such as job interviews or foreign-language conversations. These tailored experiences can lower the barriers to creating engaging simulations and give a greater number of students the opportunity to practice real-world skills. 

Together, these tools have the potential to transform education into a more interactive, continuously evolving experience—resulting in increased student engagement and improved learning outcomes. 

In healthcare, AI agents can provide more accurate diagnosis, highly personalized treatment plans and faster research-based innovations. 

AI solutions have been of particular interest to the healthcare industry in recent years, given their ability to autonomously investigate health data. They also help remove administrative burdens in busy medical institutions. In clinical settings, AI agents given access to large datasets from across departments can significantly impact time spent on administrative tasks such as billing, scheduling and resource allocation. They can also completely automate routine tasks such as prior authorizations and remote patient monitoring.

Given their proactive approach to data analysis, AI agents can also help in diagnostics, manage drug processes and monitor patient vitals in real-time, flagging potential health risks before they escalate. By integrating agentic AI into everyday operations, hospitals and medical centers can make more informed decisions, allowing providers more time to focus on high-touch, personal care. 

AI agents can play a critical role in the energy sector by enabling intelligent grid management and predictive maintenance.

For example, agents might proactively analyze data from energy equipment to predict maintenance schedules or foresee infrastructural failure. They can also autonomously balance energy supply and demand, adjusting grid operations in real-time.⁴ These task‑based agents can lower an enterprise’s carbon footprint and significantly reduce energy costs.

In the legal industry, AI agents help firms research and draft memos, streamline contract analysis and summarize case documents.

For example, a contract review agent can surface risky clauses, while legal research agents search case law and draft initial memos. For example, Thomson Reuters developed an agentic assistant for businesses and law firms, reporting a one-third average reduction in time spent on document review, research and drafting. 

AI agents offer personalized and accessible mental health support.

For example, agentic therapy chatbots provide continuous assistance through conversations in natural language, helping users manage anxiety or stress with evidence-based techniques such as cognitive behavioral therapy.

By blending emotional intelligence with continuous availability, agentic AI expands access to mental health support in a way that is scalable and private. Such conversational agents can reduce the burden on human professionals during employee shortages and expand access in areas where mental health support is not readily available. They also help reticent patients reach out for support without a fear of stigma

AI agents offer personalized shopping experiences by recommending products, predicting trends, managing inventory and powering autonomous customer service chatbots.

Intelligent merchandising agents can optimize pricing and inventory levels in real-time based on customer behavior and demand forecasts, preventing stock-outs or other interruptions.

In e-commerce, AI agents curate product selections and promotions tailored to individual customer preferences and purchase histories—or call on contextual data such as weather, location and current trends to improve results. In some brick-and-mortar stores AI agents are used to scan shelves and manage inventory in real-time. These technologies boost sales, reduce inventory issues and increase sales through targeted marketing, leading to increased customer satisfaction and higher conversion rates.

AI agents can autonomously optimize the transportation and logistics process by managing vehicle fleets, delivery routes and logistics on the large scale.

Some delivery companies use autonomous dispatching agents to assign and reroute vehicles based on traffic, weather or the urgency of orders.

Predictive maintenance systems detect vehicle issues to prevent unnecessary break-downs or wear, while intelligent routing systems reduce fuel consumption and shorten delivery timelines. These tools increase cost savings and help organizations meet their sustainability goals.

Agentic AI, combined with generative AI, has the capacity to autonomously create articles, blogs, scripts and reports tailored to specific audiences and objectives.

AI-powered design agents can also produce branded visuals or social media assets with minimal human input. In video and audio production similar tools can edit footage or synthesize voiceovers.

Unlike previous AI tools, which relied on direct and continuous human input, agentic AI allows creators to scale content output rapidly with minimal human oversight, maintaining quality and consistency throughout.

For example, the Associated Press uses AI to generate basic news articles on data-driven topics such as sports scores or financial reports, increasing the volume of content production and reducing human workloads.

Agentic AI in customer experience leads to improved customer satisfaction by increasing accuracy and often leads to cost savings.

Given sharply rising customer expectations and high levels of burnout among customer service representatives, AI agents can be useful when applied to customer experience. With their ability to improve responses over time and recall relevant customer data in real-time, agents deliver deeply contextual and hyper-personalized experiences.

Unlike traditional chatbots, which respond to customer inquiries based on predefined scripts, agentic AI can anticipate future events and take proactive action based on customer needs, increasing relevance and customer satisfaction. Equipped with natural language processing (NLP), conversational AI assistants engage in natural, dynamic conversations with customers, automatically escalating complex issues to human representatives when necessary.

Using sentiment analysis, these tools also analyze customer interactions to identify problems before they arise—or even offer and run solutions such as issuing a support tickets or refund.

Agents can act as support systems for customer service representatives, as well, organizing and retrieving relevant customer data or helping troubleshoot product issues based on customer queries. Given the abilities to interact with several systems simultaneously and retain customer data over time, AI agents are adept at providing highly personalized and proactive support. This capability enables them to deliver assistance that adapts to each customer’s evolving needs.

AI agents can detect data issues, generate dashboards for human review and flag unusual patterns.

These tools allow enterprises to continuously audit data, turning data quality from a periodic audit to an ongoing process. For reporting purposes, agents can quickly provide full narrative summaries of complex analytical patterns. And query agents provide data accessibility to non-technical audiences, allowing them to query large datasets in natural language. 

In human resources, AI agents reduce administrative burden and significantly improve the employee experience.

In the hiring process, HR-focused AI agents perform multiple time-consuming tasks including resume screening, resume analysis, candidate ranking and interview scheduling. When a candidate is hired, personalized onboarding experiences tailored by AI provide new employees with individual training schedules and plans.

For current employees, agentic AI assistants can provide several critical resources to the workforce, including personalized training recommendations based on role, experience or career goals. Meanwhile, these autonomous systems also handle administrative support requests such as responding to employees’ FAQs, managing leave requests and ensuring compliance.

For example, IBM’s AskHR fully automates over 80 common HR requests. This capability significantly increases the time HR leaders can allocate championing the employee experience and engaging in more nuanced, creative tasks. And, using AI for talent management, HR leaders gain insights into the factors driving successful long-term hires through data analysis.

Using such agentic AI solutions, HR leaders save time and money through the recruitment and talent management process, as well as standardizing the hiring and promotion process through unbiased, data-driven input.

Intelligent agents in IT operations autonomously manage infrastructure, detect anomalies and optimize system performance, reducing downtime and operational risks.

Agents can also act as assistants to developers, continuously monitoring a system’s health, troubleshooting and deploying fixes autonomously. Agents programmed to increase cybersecurity can detect threats in real-time, taking proactive measures to prevent attacks.

And, increasingly, agents act as developer tools to assist programmers. For example, NASA engineers recently launched an agent for use in the Jet Propulsion Laboratory. The agent, which interacts with specific robotics system languages, helps robot developers inspect, diagnose and operate robots through natural language prompts.

AI agents autonomously provide consumer analysis, content generation and continuous monitoring to improve marketing activities.

AI agents have several applications in marketing, given the vast amounts of data marketing departments ingest daily—and how many competing offers customers encounter. Today, some agentic AI tools are transforming the product discovery process as consumers ask agents for shopping advice rather than searching online themselves.

In marketing and e-commerce, AI agents can autonomously perform various communications and advertising tasks. This capability might involve managing campaigns, creating customer personas, personalizing content and optimizing ad performance in real-time. While previous automation and AI technologies can manage these tasks, they relied on more oversight and frequent user inputs to perform effectively.

Using predictive analytics, AI agents can analyze customer behavior to identify the best times or messaging strategies for a specific campaign automatically. They then pass off that information to agents who can schedule the communications themselves. And with proactive analysis, these technologies continuously create robust customer personas based on vast troves of data, providing other insights for marketing campaigns.

Social media AI chatbots can monitor a brand’s mentions, engage with users and generate relevant responses more accurately than their non-agentic predecessors. In addition, agentic AI providing customers with product recommendations can pull from various tools, datasets or previous user behaviors to accurately identify their needs.

For instance, AI agents can suggest vacation bookings tailored to multiple people’s travel preferences and external factors such as the weather.

In operations, organizations see the greatest productivity gains in agentic workflows across systems.

AI agents are increasingly taking on coordination work that once took hours. Internally, they can monitor inventory levels or flag anomalies in supply chains. In customer-facing operations, agents can handle multi-step service requests from verifying orders to initiating refunds.

Cross-system orchestration compounds the value of these multi-system agentic workflows over time. A single process such as a customer complaint might involve a ticketing system, a shipping provider’s API and an internal knowledge base. Agents can query all three instantaneously and synthesize the findings, reducing costly bottlenecks. And increasingly, agents are used for process auditing. By continuously reviewing logs, expense reports and compliance checklists, agents surface potential issues for human teams to review. 

In sales, AI agents automate tasks and streamline access to customer data.

Agentic AI embeds deeply into existing tools such as customer relationship management (CRM) software to access customer data such as the previous interactions and consumer preferences. Agents can assist in the lead generation and qualification process, scoring potential leads and prioritizing follow-ups with customers most likely to convert.

In the lead nurturing process, AI agents autonomously communicate with potential customers—through email, chatbots or voice assistants, for example—to provide personalized outreach and answer questions. These agents’ ability to store prospective client data and handle multiple leads simultaneously makes them easy to scale. Organizations can grow without adding manual effort and the system continues expanding as demand increases.

This scalability allows organizations to handle rising demand without increasing manual effort. Given access to historical data, these tools forecast trends and potential sales opportunities, allowing sales teams to make data-driven decisions and allocate resources most effectively.

Internally, AI agents can be a great asset to sales teams. By transcribing and analyzing sales calls, surfacing relevant lead data before meeting or helping sales agents schedule meetings. By providing real-time feedback to sales departments, AI agents help their human counterparts continuously improve performance.

In software development, AI agents improve productivity and documentation, automate testing and bug fixing, and reduce costs across the software development lifecycle (SDLC).

In recent years, these agents have moved significantly past autocomplete into genuinely agentic workflows. Given a ticket or bug report, an agent can explore a codebase, identify relevant files and run tests. This is particularly valuable for repetitive tasks such as upgrades or migrations.

But more advanced use cases involve agents operating across the full SDLC—integrating specifications, working up implementation and iterating based on reviewer feedback with minimal intervention until a change is ready.

For example, IBM’s coding partner Bob is currently used by 10,000 IBM developers across its software, consulting and infrastructure departments. The tool communicates with both agents and humans, providing feedback in natural language. It has also increased software developers’ productivity by 45%: “We’ve built an ecosystem that’s pretty powerful in terms of making not only developers productive,” Neel Sundaresan, a General Manager of Automation and AI at IBM, recently told IBM Think, but in also kind of enjoying their jobs.” 

In supply chain management AI agents help business leaders make more accurate decisions about vendors and streamline the contracting process, reducing errors and bringing down costs.

One of the central advantages of agentic AI over traditional models is its ability to act dynamically, analyzing data and modifying tasks without human instruction in real-time. This capability makes the technology well suited to the supply chain, inventory management, risk assessment and procurement process. AI agents can streamline the supplier selection process, evaluating potential suppliers based on their cost-effectiveness or sustainability metrics and flagging potential risks.

Supply chain agents also process such as contracting and purchase ordering, reducing manual effort and ensuring accuracy in supplier management. Agents’ ability to cross-reference these processes against criteria such as inventory levels adds an extra level of verification to the procurement process, preventing disruptions.

When data is centralized, agentic AI provides valuable insights, allowing enterprises to make more accurate decisions in both the short- and long-term. Agents can create detailed cost analysis and identify opportunities to cut costs or forecast demand based on several factors including market conditions and global events. The technology can also be a critical compliance management tool, proactively monitoring transactions and internal processes based on an organization’s specific regulatory environment.