Top AI agent use cases across industries

AI agents are transforming the way businesses operate through their autonomous decision-making, from self-driving cars that navigate cities to virtual assistants that automate customer support. Deloitte predicts that by 2027, half of the companies that use AI will have launched AI agents that will act as intelligent assistants with minimal human intervention.

This post examines what makes an AI system an agent and explores how autonomous agents are being applied in various domains.

What makes an AI system an "agent"?

For a system to be considered an AI agent, it must possess several core capabilities that distinguish it from ordinary software. These capabilities enable the agent to act intelligently and interact meaningfully with other systems or humans:

• Autonomy: The AI agent should function without direct human intervention and control.
• Reactivity: AI agents must be reactive to their environment, meaning they should perceive changes in their environment through sensors or data inputs and respond in real time. For example, a chatbot can recognize a user's complaint as text input and respond with a troubleshooting step. At the same time, a cybersecurity agent can observe unusual login activity and raise an alert or block access.
• Proactive behavior: At the same time, they should exhibit proactive behavior in taking initiative to achieve goals rather than just reacting.
• Social competence: An AI agent should exhibit social competence, meaning the ability to interact with other actors or people, including not only humans but also other AI agents or software systems. For example, a virtual assistant could collaborate with a calendar service (another software agent) to schedule meetings.

AI agents can also be categorized based on how they make decisions and what knowledge they use, with common distinctions made between simple reflex, model-based, goal-based, and utility-based agents:

• Simple reflex agents: A simple reflex agent only reacts to the current perception (input) and predefined rules, ignoring the entire previous history. These agents are fast and responsive, but they're also limited because they make decisions solely based on current input without considering past interactions or context.
• Model-based agents: By contrast, model-based agents maintain an internal state. For example, they remember previous user inputs or track changes in the environment, which allows them to make more informed decisions over time.
• Goal-based agents: These agents are driven by goals and choose actions that bring them closer to achieving these goals. Before acting to achieve the goal, they can simulate or predict the outcomes of actions, such as by using search or planning algorithms.
• ‍Utility-based agents: These go a step further by assigning a utility value to world states and behaviors, with the overarching goal of maximizing their "satisfaction" or performance measure.

AI agents in autonomous vehicles

Autonomous vehicles are one of the most impressive applications of AI agents. Self-driving cars, such as those from Waymo and Tesla, use internal AI agents to perceive their environment, navigate, and make split-second driving decisions.

AI agents in autonomous vehicles incorporate a decision-making logic that consists of three parts: perception, decision-making, and control. An autonomous vehicle uses various sensors, including cameras, radar, ultrasonic sensors, and LiDAR, which collect real-time data about its environment. 

During the perception phase, this raw sensor data is processed to detect and classify objects, such as pedestrians, other vehicles, road signs, and obstacles. Techniques such as sensor fusion combine data (by merging camera vision and radar ranging, for example) to create an accurate view of the environment. In the subsequent decision and planning phase, this understanding of the environment is used to plan the car's behavior (such as path planning). 

Finally, in the control phase, all planned actions are converted into low-level commands for the vehicle's actuators, like the steering angle, accelerator, and brakes. AI agents in autonomous driving are developed with strict safety protocols and redundancies. Advanced training techniques are used to enable them to operate in open environments with unpredictable variables. 

For example, simulation and reinforcement learning enable agents to learn from virtual driving scenarios. Companies like Foretellix use scenario-based verification platforms to generate millions of virtual driving scenarios, uncovering rare and critical edge cases that are difficult to test in the real world.

While autonomous driving presents considerable technical challenges, the benefits are also significant. AI-driven agents have the potential to significantly reduce accidents by eliminating human error, which is the cause of approximately 90 percent of all accidents today. An AI that controls a vehicle won't hold a mobile phone while driving, exceed the speed limit, or drive under the influence of alcohol. Data from Waymo's autonomous vehicles indicate a significant reduction in the number of accidents involving personal injury compared to human drivers. Additionally, AI agents can enhance traffic efficiency through intelligent coordination. For example, they can strategically adjust speeds to allow smoother merging at intersections and roundabouts to reduce stop-and-go traffic and minimize congestion.

AI agents in intelligent virtual assistants

One of the most well-known applications of AI agents is the intelligent virtual assistant, such as Google Assistant, Siri, or Alexa. These agents offer voice-controlled service around the clock, with instant response times, and can handle a wide range of user requests. For example, users can set reminders or ask questions and receive answers.

The typical architecture of a virtual assistant comprises several AI components that operate in sequence. Intelligent AI assistants rely on natural language processing (NLP) to transform user input in speech or text into machine-readable intent. When the input is spoken, an automatic speech recognition model converts the user's speech into text. Then, an NLP module or model interprets the user query. 

For example, if a user says, "I need to rebook my flight for next Wednesday," the AI determines the intent (flight rebooking) and extracts entities (new date, possibly which flight from the context). Ultimately, LLM systems can generate human-like and contextually nuanced responses. A virtual assistant can also connect with databases and services to retrieve customer information from a CRM system, check the inventory of an ERP system, or create a support ticket in a help desk platform.

Virtual assistants have many advantages. For instance, AI-powered chatbots used in customer support can process large volumes of text-based requests in parallel, thus reducing wait times and operating costs. This makes them particularly effective for routine or repetitive tasks. In fact, chatbots have been proven to reduce customer service costs up to 30 percent by automating up to 80 percent of routine requests. Additionally, AI assistants can also deliver personalized customer experiences by analyzing past interactions and user data to tailor recommendations, responses, and support to individual preferences.

AI agents in cybersecurity and threat detection

Cybersecurity is also being transformed by AI, where companies like Darktrace and CrowdStrike are using AI agents to observe networks and systems to enable autonomous threat detection and response.

To be effective, AI agents must integrate with broader security ecosystems to enhance an organization's security posture and facilitate faster, automated threat containment. Examples of such ecosystems include security information and event management (SIEM) tools like Splunk, IBM QRadar, and cloud security APIs (such as Amazon GuardDuty, Azure Sentinel, and others). From a compliance perspective, AI-driven cybersecurity must align with standards like NIST SP 800-53, ISO 27001, GDPR, and HIPAA. Further compliance measures may include the support of audit trails, data retention policies, and explainability.

If these constraints are met, AI agents offer several benefits. The agents can react in milliseconds and execute containment actions such as quarantining endpoints or blocking network flows before a human analyst even notices an alert. They can also detect anomalies, such as deviations from expected behavior within networks or systems. For instance, Darktrace's Enterprise Immune System uses unsupervised learning to detect anomalous behavior without prior knowledge or information about the threat. 

Furthermore, these agents provide round-the-clock threat monitoring. This "always-on" defense posture can drastically reduce dwell time and exposure. At the same time, AI agents reduce the burden on the security team. For example, Charlotte AI reportedly reduces CrowdStrike analysts' triage workload by over 40 hours per week, allowing them to focus on more in-depth investigations.

That said, technical challenges still remain. Agents require AI-driven threat modeling, which must be trained on diverse telemetry, including logs, flows, and behaviors across various industries and attack types. This involves supervised learning for known cybersecurity threats and unsupervised learning to detect unknown threats. Handling false positives is also a big hurdle, but it can often be addressed through feedback from analysts as part of the post-analysis process. At the same time, AI agents must react instantly and process massive amounts of telemetry data in real time, including network traffic and system behavior. To support this low-latency processing, companies require a reliable and scalable data streaming platform. 

These platforms must process and analyze incoming telemetry data instantly. Platforms like Redpanda offer a real-time streaming infrastructure that can efficiently handle this workload. Lastly, as threats evolve daily, AI agents must constantly be updated with new threat data to ensure resilience against adversarial adaptation.

AI agents in healthcare and diagnostics

Healthcare is also advancing through the use of AI, particularly in diagnostics. AI agents equipped with computer vision can analyze X-rays, MRIs, CT scans, and other images to identify diseases—basically acting as a "second" pair of artificial eyes to support a doctor's assessment.

This introduces several advantages. For example, AI's fast processing speed allows it to detect diseases earlier than humans, which is particularly important in cancer diagnostics, where earlier detection could improve survival rates. AI models have already shown promise in detecting signs of pancreatic cancer. 

AI agents are also consistent; unlike human doctors, whose opinions on the same scan can vary, AI agents provide a more consistent interpretation, which could help reduce diagnostic errors. In addition, AI agents can save time by automating routine analyses, such as prescreen scans, and distinguishing between "normal" and "suspicious" cases, allowing radiologists to focus more on the latter.

Despite their benefits, the use of AI agents in healthcare still faces some challenges. Data privacy and regulations (such as HIPAA) strictly regulate patient data. Any autonomous agent must operate within these rules and ensure the appropriate anonymization and security of sensitive data. Furthermore, clear boundaries of responsibility need to be defined. The agents should support medical staff but cannot replace them; therefore, any critical decisions made by an AI must be reviewed and confirmed by a human expert. 

Another challenge is that medical data can be incomplete, noisy, or distorted due to factors such as poor image quality or missing patient information. This can compromise the reliability of AI predictions, leading to overconfident or misleading results. To avoid this, AI agents should be designed to handle uncertainty, such as by flagging low-confidence results for further review. Qualified medical professionals must always review critical AI decisions to ensure safety and accuracy.

AI agent implementation considerations

Deploying AI agents in real-world environments requires careful data and infrastructure planning. Important considerations include:

• Training data quality: The performance of AI depends on the quality of its training data. Applications such as autonomous vehicles, customer support bots, or cybersecurity tools require large, diverse data sets that reflect real-world conditions.
• Data pipeline design: At the same time, these types of applications require robust data pipelines to efficiently collect, clean, and transmit representative data with minimal latency.
• Human-in-the-loop vs. full autonomy: Another important design choice is whether AI decisions are made autonomously or validated by humans. In safety-critical areas such as healthcare, it may be essential to keep a human in the loop as a safety net to validate an AI's decisions.

• Infrastructure and deployment strategy: AI agents require significant computing resources. Depending on your latency, scalability, and deployment needs, you might choose between cloud, edge, or hybrid architectures.
• Real-time streaming and processing needs: For agents that require responding to real-time telemetry, such as self-driving systems that react to sensor inputs, high-throughput and low-latency data streaming become essential. 

Tools like Redpanda can support these workloads by providing a platform optimized for fast and reliable event streaming. For example, a cybersecurity AI agent could leverage Redpanda to stream and analyze network logs in real time, detecting and containing anomalies before they escalate.

Conclusion

AI agents are being used across industries to automate decisions, increase efficiency, and solve complex problems in real time. The practical value of AI agents lies in their ability to perform focused, goal-oriented tasks autonomously and adaptively, from autonomous vehicles that increase road safety to virtual assistants for 24/7 support to cybersecurity agents that detect threats before they escalate.

As AI agents become more widespread, companies need to lay the right foundations to support them at scale. This includes building a powerful data infrastructure and having the ability to work with real-time information. Now that you understand the importance of real-time data for AI agents across industries, check how Redpanda's high-performance streaming platform keeps your AI systems responsive and efficient. If you have questions, ask away in the Redpanda Community Slack.