Agentic AI is quickly emerging as business technology’s next big thing. It’s predicted that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.
Agentic AI distinguishes itself from current generative AI by its autonomous decision-making. These advanced agents can understand complex issues, reason through scenarios, make independent choices, and execute tasks end-to-end, all while learning and refining their abilities.
Together, let’s explore agentic AI’s use cases across industries. We’ll start by sharing some real-world examples to highlight its automation capabilities. After reading, you should have a better sense of how agentic AI can empower your organization.
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10 Agentic AI Examples and Use Cases
From healthcare to manufacturing, let’s dive into ten ways agentic AI is set to revolutionize how businesses complete tasks.
1. Customer Service
Gartner predicts agentic AI will resolve 80% of common customer service issues without human intervention by 2029. Unlike traditional chatbots relying on pre-programmed scripts and keywords, agentic AI learns from context, adapts to unique customer needs, and implements solutions.
Consider a customer reaching out about a delayed shipment. While a traditional chatbot might only provide tracking or escalate to a human, an agentic AI system could:
- Access and analyze live shipping data.
- Determine the delay’s cause.
- Offer solutions like expedited replacement orders or partial refunds.
- Update records and carry out the chosen resolution independently.
2. Application Development and IT Operations
Agentic AI is set to innovate the software lifecycle by changing how software is built, deployed, and maintained. Notably, four in five developers anticipate AI agents becoming as crucial to app development as standard software tools.
AI agents can assist with generating large segments of code and provide real-time suggestions for better coding. AI can also automate software testing by generating test cases, running tests, and analyzing outcomes.
These tasks can accelerate the development process, reduce coding errors, and free up developers to focus on more complex challenges.
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3. Application Security and Cybersecurity
Over 30,000 new vulnerabilities were discovered in 2024 – a 17% year-over-year increase. As cyber threats grow in number and sophistication, agentic AI is becoming a critical ally to bolster security postures. It outperforms traditional security tools like firewalls and antivirus software to provide a new level of automated defense.
For instance, it offers a powerful threat detection system. Agentic AI can analyze factors like application code, network traffic, user behavior, and system logs to flag anomalies or suspicious patterns. AI agents can then prioritize these vulnerabilities by risk level and automatically apply patches or recommend fixes, shrinking the opportunity window for attackers.
4. Healthcare
Agentic AI is a proactive partner to assist patients, support clinicians, and optimize administrative workflows – improving patient outcomes and streamlining operations.
Take these applications for example:
- Personalized patient monitoring and intervention: Agentic AI can continuously monitor patients with chronic conditions via wearable devices. Upon detecting potential risks, it can alert patients and providers, schedule follow-ups, or even adjust medication within approved parameters, leading to better illness management and fewer hospitalizations.
- Diagnostic support for clinicians: AI can generate potential diagnoses and treatment options by analyzing patient data (e.g., symptoms, history, lab results, etc.). This assists clinicians in making more informed and faster decisions, particularly in complicated situations or with staffing limitations.
- Streamlined office tasks: Agentic AI automates repetitive tasks like appointment scheduling and reminders, insurance claim processing, and record management. As a result, facilities can reduce overhead, minimize errors, and allow staff to focus more on patient care.
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5. Drug Discovery and Development
Agentic AI can significantly accelerate the intricate, time-consuming process of drug discovery and development to bring treatments to patients sooner.
One promising use is ADME (Absorption, Distribution, Metabolism, Excretion) profiling, which predicts drug behavior in the body. A major hurdle is drug candidate failure in later stages due to poor ADME properties or toxicity popping up. Agentic AI can predict these properties early by analyzing molecular structures and historical data, filtering out unfavorable candidates and prioritizing promising ones.
AI also optimizes clinical trial design and execution. AI agents can assist in patient selection, predict treatment responses, optimize dosages, and monitor trial progress in real-time, potentially increasing success rates and lowering costs.
6. Manufacturing
Manufacturing is increasingly adopting AI to cut costs, boost efficiency, and improve product quality.
Key agentic AI examples include:
- Production scheduling: Analyzing data pulled from order volumes, machine availability, material inventory, and delivery schedules can help adjust and optimize production schedules for smoother operations.
- Predictive maintenance: Agentic AI can monitor machinery and equipment performance using sensors and historical data to forecast potential failures. It can then help schedule maintenance, order parts, and alert human technicians.
- Quality control: AI can analyze data from cameras and sensors to identify product defects in real-time. These anomalies can trigger corrective actions, and provide feedback to prevent future issues.
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7. Inventory Management
AI helps optimize inventory supply and demand, ensuring the right products are available in the correct quantities.
Agentic AI can analyze a variety of data points, including historical sales, seasonal trends, marketing campaigns, economic indicators, and even social media sentiment, to generate accurate demand forecasts. Based on these predictions, AI agents can automatically create purchase orders for optimal quantities, minimizing stockouts and overstocking to cut unnecessary costs.
8. Supply Chain Management
Automation and real-time insights empower supply chain resilience by helping to proactively adapt to changing external conditions.
AI agents use cases include:
- Proactive risk management: Agentic AI continuously monitors operational data, analyzes potential supply chain risks (e.g., natural disasters, geopolitical events), and develops mitigation plans. During disruptions, AI can identify alternative suppliers, reroute shipments, and communicate with stakeholders to minimize impact.
- Smart transportation and logistics: AI agents can dynamically plan and optimize live transportation routes, considering influences like traffic congestion, weather conditions, and delivery schedules. These agents can also select efficient carriers, negotiate rates, and reroute shipments for faster delivery and lower costs.
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9. Financial Services
While AI currently aids fraud detection in the financial services industry, agentic AI can manage complete financial processes, such as investment portfolios. An AI agent could analyze market data, assess risk, make investment decisions (buying, selling, rebalancing), and adapt strategies based on market conditions and individual financial goals.
Plus, AI agents can handle these customized cases at scale without human oversight, democratizing access to personalized wealth management.
10. Government
Agentic AI can help state and local agencies improve their engagement with citizens while boosting internal efficiency and productivity.
AI agents can handle a wide range of inquiries, process applications for permits and benefits, provide personalized information on government services, and even guide citizens through intricate bureaucratic processes.
This results in quicker response times for citizens and less administrative work for employees. For example, an AI agent could process a simple driver’s license renewal by verifying details and then issuing the new license autonomously.
>> Related Read – Top 5 AI Adoption Challenges for 2025: Overcoming Barriers to Success
AI Agent Useful Case Study Roundup
Now that we’ve covered some agentic AI examples, here are real-world use cases showing their tangible business impact.
- Mercedes-Benz has added an automotive AI agent, MBUX Virtual Assistant, to its CLA class of cars to give owners more detailed and personalized conversational responses about navigation, points of interest, and more. Users can converse naturally with the AI agent and get answers to questions like: “Could you guide me to the nearest fine-dining restaurant?” It can even answer follow up questions like: “Does the restaurant have good reviews?”
- Pharmaceutical company Bayer has leveraged agentic AI to predict cold and flu outbreaks. The company used trend data based on Google searches (e.g., searching medicine or symptoms) as well as external data such as weather information and public flu reports. Bayer’s marketing team then used this data to help reach people with their products to alleviate symptoms.
- AES, a global energy company, uses agentic AI to automate and streamline its energy safety audits to improve audit quality and ensure safety compliance and best practices. This has resulted in a 99% reduction in audit costs, a time reduction from 14 days to one hour, and an increase of 10-20% in accuracy.
Jumpstart Your Agentic AI Journey
Whether it’s helping supply chains run smoother, improving patient care, or speeding up government processes, agentic AI is unlocking productivity and innovation across industries.
Embarking on your agentic AI journey requires a trusted and knowledgeable partner. Converge Technology Solutions is positioned to meet you wherever you are in your AI journey. If you’re ready to drive tangible results and future-proof your operations with agentic AI, including custom-built AI agent examples for your organization, let’s talk.
