Agentic AI for Businesses in 2025:
Examples, Use Cases & Benefits

Agentic artificial intelligence (AI) is transitioning from pilot projects to concrete applications for business-critical processes. You can find agentic AI examples in all industries, and use cases are expanding.

Companies are adopting these systems despite implementation challenges and inherent risks. According to a 2025 Gravitee survey, approximately 72% of medium-sized companies and large enterprises currently use agentic AI, and an additional 21% plan to adopt it within the next two years. The global market is predicted to grow from $5.2 billion in 2024 to $196.6 billion in 2034.

Should you embrace this technology now or wait until it matures? Our article breaks down current real-life agentic AI use cases and shows how the technology can benefit companies.

Agentic AI is a semi-autonomous, self-learning, and deterministic system capable of handling complex tasks. It can learn from past interactions, make real-time decisions, plan execution, adjust behavior based on real-time data, and coordinate other tools and APIs.

Here’s how it works: first, you assign an objective and establish constraints (rules). Agentic AI then interprets your goals, breaks them into subtasks, and plans how to accomplish all the tasks. The system uses third-party apps and databases, adjusts execution of its plan based on output, and studies the results to learn from mistakes.

You can learn more about the technologies behind agentic AI workflows in our in-depth guide.

Businesses prefer agentic AI over traditional rule-based automation and fully autonomous agents for several reasons:

• AI can handle business-critical processes that span multiple teams, tools, and decision layers.
• AI agents assist human employees by retrieving and interpreting data or notifying teams about anomalies.
• Agentic AI workflows constantly check conditions, adapt to tasks, and escalate to a human when their judgment is required.
• Agentic AI operates within strictly defined boundaries, and its decisions remain auditable, whereas autonomous AI agents act with fewer constraints and control.

Now that you have a general answer to the question “What is an Agentic AI?” let’s look at some examples to help you truly understand.

What are some real-world applications of agentic AI that your company can potentially implement now? Let’s look at some key examples in different sectors.

1. Finance

Agentic AI introduces multistep workflows that continuously analyze high-velocity financial data. Use cases for agentic AI in financial organizations include adjusting credit scores, automating Know Your Customer (KYC) checks, calculating loans, and continuous monitoring of financial health indicators.

The systems can fetch data beyond traditional sources, including customer relationship management (CRM) systems, payment gateways, banking data, credit bureaus, and sanction databases. Moreover, agentic AI can cross-reference entity names, addresses, and social media platforms to expose shell companies and individuals tied to sanctioned actors.

Agentic systems can learn more about customers from behavior signals than traditional AI applications, including information about transactions, income, and purchases. When a transaction deviates from a typical pattern, it can trigger additional checks on activity. At the same time, the model continuously learns from customer responses and the security team’s input.

Examples:

• PayPal provides AI agents and workflows that handle payments, order tracking, invoicing, product discovery, and fraud prevention.
• Wolters Kluwer’s CCH agentic AI can test financial assumptions, forecast economic indicators, and simplify complex reports.

2. Healthcare

In healthcare organizations, real-world applications of agentic AI help manage operational processes and conduct clinical trials. For example, it can update electronic health records (EHRs) based on information from laboratory systems, wearable devices, telehealth visits, and handwritten notes.

Hospitals use agentic AI to optimize patient flow, schedule patient meetings, predict bed occupancy rates, and manage staff. But use cases extend beyond documentation management and scheduling.

Agent-based AI can detect signs of health problems based on data from remote monitoring tools or patient scans. Doctors and nurses use AI to get suggestions on potential diagnoses and treatment options. AI can personalize communications with patients, including educational materials, medication suggestions, and basic motivational support.

Examples:

• Seattle Children’s Hospital integrates agentic AI that processes data from CSWs, medical literature, notes, and images to deliver evidence-based clinical information to healthcare providers.
• eClinicalWorks uses agent-based AI that extracts patient data from incoming documents and integrates it with patient records. Another agentic AI example is an agent that answers common patient questions via chat, text, or voice.

3. Insurance

Use cases of agentic AI in the insurance sector include account management, loan processing, and underwriting. If a customer’s policy is about to expire, the system can offer renewals and reschedule billing cycles. Companies can also configure AI to suggest insurance products based on specific rules. For instance, it may recommend (or avoid recommending) insurance depending on the risk of upcoming weather-related flight cancellations.

Agentic AI can process insurance claims based on information in emails, telephone calls, handwritten notes, and images. It processes the data, verifies it against the customer’s policy terms, and cross-references for prior claims and suspicious activity, like photo metadata inconsistencies or unusual submission patterns.

These systems don’t necessarily rely on static risk models or prefilled applications for assessment. They can pull signals from satellite imagery, telematics, biometric data from health apps, and regional crime trends. For example, an agentic AI system may adjust vehicle insurance or life insurance costs based on the frequency of speeding tickets.

Examples:

• LTIMindtree and Boomi are working on agentic AI systems for claims processing, vehicle damage assessment, and customer service automation.
• Counterpart rolled out an agentic AI insurance platform that performs underwriting, risk mitigation, and claims management.
• Allianz Partners USA uses AI agents for claims processing and customer support.

4. Retail

AI agents allow companies to adjust pricing in response to moment-to-moment variables, such as demand signals, competitor pricing, and local events. Other use cases of agentic AI in retail include having it make operational decisions about things like inventory supply projections, warehouse transfers, and order allocations.

Let’s say a customer on your website abandons a cart. Rather than triggering basic scripts, agentic AI can detect the reason for abandonment and craft a multistep sequence across channels. For instance, if a user abandoned the purchase on the shipping selection page, the AI agent may send a follow-up email offering local pickup options. For high-value customers, the AI agent might alert a human representative to make contact or trigger a loyalty reward.

On websites, AI agents can personalize suggestions and discounts based on page visits, bounce rates, and clicks. If users interact with a page only briefly, the AI agent can trigger a time-limited discount. In physical stores, if IoT sensors show decreased customer engagement with certain products or areas of the store, a system might suggest rearranging featured products.

Examples:

• Walmart designs generative agentic AI for various retail workflows, such as displaying item comparisons and recommendations, and offering shopping assistants (personal agents) to website visitors. Other agentic AI use cases at Walmart include planning pricing strategy and inventory optimization.

5. Supply chain management and logistics

Companies usually implement a significant degree of automation, predictive maintenance, and other AI-powered tools. Agentic AI can execute decisions semi-autonomously across the entire supply chain with few manual interventions.

For example, where traditional AI can forecast demand and flag problems, an agentic system can reroute shipments, reissue delivery times, inform all affected parties, and, with enough rights, approve new routes. It can analyze why a specific inventory dip happened and suggest remediation options based on risks, customer impact, vendor agreements, and historical error rates.

A good example of agentic AI is its ability to orchestrate multiple tools. If a company has a separate logistical application for fleet routing, one for warehouse slotting, and another for vehicle management, an AI agent can optimize logistics workflows across all those applications.

Examples:

• Amazon’s agentic generative AI improves last-mile delivery routes, which saves them up to $100 million annually.
• DHL’s agentic system predicts shipping demand, optimizes routes, and controls warehousing operations, saving up to 15% on operational costs.

6. Manufacturing

In many factories, software generates threshold-based alerts by analyzing telemetry data from PLCs, SCADA, and MES. But they often have management systems that create silos because of fragmented tech stacks. Agentic AI can manage the different tech stacks to accomplish multifaceted tasks.

For example, if a CNC machine’s torque readings deviate from its baseline, the system may reduce feed rates, reassign tasks to other machines, trigger an inspection, reschedule affected jobs, and suggest ordering replacement parts.

Rule-based systems and hardcoded production sequences that rely on static patterns can struggle in high-variability, low-volume (HMLV) environments that produce a wide range of products. However, agentic AI adapts to product design changes, finds component alternatives, and reoptimizes workloads if a machine fails.

Examples:

• Siemens AI models analyze IoT data to monitor equipment health and predict machinery failures proactively.

7. Legal services

Many time-consuming legal tasks can be simplified with AI agents and workflows. Examples of agentic AI in practice include analyzing contacts, tracking obligations and deadlines, summarizing complex documentation, and assessing risk. The agentic AI system can identify gaps, contract expiration or renewal terms, as well as differences across vendor templates.

Agentic AI can automate many due diligence processes. In addition to internal documentation, it can cross-reference clause libraries, detect terminology deviations, highlight missing provisions, and flag intellectual property issues. Advanced systems can evaluate a company’s infrastructure and technical readiness, codebase quality, security and privacy tools, and human resources.

Agentic systems with generative AI capabilities can draft contracts, cease and desist letters, claims, NDAs, and more. Some can even generate multiple versions based on tone, legal positioning, and urgency. While legal specialists still verify the documents, AI reduces drafting time.

Examples:

• JPMorgan’s platform extracts data points from legal documents, tables, and images, saving over 360,000 hours of manual review every year.
• Hogan Lovells uses agentic AI to analyze volumes of contracts and sensitive documents, increasing document review speed by 40%.

8. Farming and agriculture

Agentic AI business use cases in agriculture include precision crop management. An agentic AI system can continuously ingest data (from sensors, satellite images, weather stations, analytical platforms, etc.) to directly optimize a system of irrigation controllers, fertilizer injectors, field drones, and other components. 

If a heatwave is forecasted, the AI system may suggest an earlier harvest window, reschedule equipment dispatching, and alert operators. Monitoring real-time variables and executing decisions to manage these components improves sustainability targets and minimizes waste. 

In livestock management, agentic systems can handle complex scenarios by controlling multiple tools to optimize feeding, medication, and herd control. For example, if animals show signs of illness (indicated by sensors that document reduced feed intake, temperature drop, or irregular movement), the AI system can isolate animals via automated gates, adjust their feeding regimen, and alert the veterinary team with a summary of observed deviations.

Examples:

• John Deere See & Spray uses advanced vision systems and agentic workflows to distinguish crops from weeds, reportedly saving 70% on contact chemicals.
• IBM’s Maximo is an asset management system that can manage greenhouses, track irrigation system components, optimize equipment performance, suggest lower-emission strategies for machinery, and more.

9. Software development and IT

Companies can speed up IT operations and software lifecycle with agent-based systems. Some real-world examples of agentic AI include adaptive IT service management systems that continuously monitor infrastructure telemetry and investigate underlying issues. Depending on the rules, the AI system can propose and execute corrective actions.

Agentic AI can identify and mitigate cyber threats without waiting for human intervention. Agentic systems are more sophisticated than traditional signature-based detection tools because they can learn from previous incidents, recognize intent patterns, and proactively block threats.

Agentic AI assistants integrated into software development can generate boilerplate code, refactor syntax according to guidelines, and debug runtime issues. In DevOps, they can parse CI/CD logs, detect regressions, identify configuration mismatches, and point out security vulnerabilities.

Examples:

• Amazon’s agentic AI, Transform, can automate refactoring and migration tasks across environments, help decompose legacy systems, rewrite code, optimize networks, and coordinate transformation plans.

10. Customer support

Autonomous customer service agents are quite common examples of agentic AI systems that are employed across the industries we mentioned above.

These systems assess customer queries and autonomously resolve tickets. Unlike virtual assistants and chatbots, agentic AI can invoke more databases, maintain longer contextual windows, and use current and past interactions to personalize responses.

The examples of agentic AI applications we discussed enable your systems to make data-driven decisions faster. This can enhance many aspects of your company, but it can also introduce risks.

There will certainly be more diverse agentic AI examples after 2025, so the advantages and challenges may look different in the future.

Agentic AI industry use cases expand every year as the technology evolves. It’s important to consider where it’s heading and what challenges to expect.

• ➤ Agentic AI as a competitive edge. Given the fact that 93% of professionals surveyed by Gravitee either actively use or plan to implement agentic AI to automate tasks, reduce overhead, and improve customer experience, organizations that ignore this technology may fall behind their future-looking competitors.
• ➤ Mass resolution of customer service issues. According to Gartner’s 2025 article, agentic AI is expected to resolve 80% of user issues without human assistance by 2029. It may reduce the support costs by 30% and completely redefine the customer experience.
• ➤ Integration with edge computing. Edge computing brings computation closer to data sources by processing it locally, rather than sending all data from IoT devices and apps to distant cloud servers. We expect more companies that require fast responses to adopt this technology to reduce latency in field service operations and to enable the function of devices at remote sites with limited connectivity.
• ➤ Priority on trust. According to Salesforce’s 2024 State of the AI-Connected Customer report, users have more trust in AI systems with transparent algorithms (42%), greater control (32%), and explainable outputs (31%). These factors will become critical features for future agentic AI systems.
• ➤ Emphasis on regulatory compliance. The European Union’s AI Act classifies agentic AI as high-risk systems that require clear oversight and full assessments for compliance with the act. This means organizations will have to document data sources, ensure models are trained on appropriate datasets (e.g., in terms of size, bias, and compliance with privacy and other regulations), embed interpretable (explainable) models, and conduct external audits to validate their systems.

If you’re serious about implementing agentic AI, you need to build modular, upgradable systems. And if you lack internal skills for that, we recommend partnering with experienced developers.

Companies that understand how to use agentic AI can unlock new levels of productivity, control, and customer service. But understanding the use cases doesn’t mean you can build an accurate model on your own that aligns with your business.

You’ll need AI models tuned to avoid bias, trained on your operational data, and maintained for accuracy, among other things. This requires expertise that many teams lack.

DevCom can build purpose-driven, explainable agent-based AI systems that integrate into your stack and fit with your workflows.