Agentic AI: The next evolutionary stage of artificial intelligence

Instead of automating processes manually step by step, AI agents take over the coordination. When different agents work together in an orchestrated manner, they can plan, delegate, and perform tasks. They can also display progress and provide progress analyses. For example, an agent system in project management can prioritize tasks on a daily basis, analyze status messages, and detect blockers at an early stage.

AI agents can identify, structure, and evaluate data sources independently – having to specify each query. In market research, for example, an AI agent system researches current trends, aggregates customer feedback, identifies patterns, and creates concrete recommendations for action on this basis.

AI agents can interact with users and tools, and they have a data store so that conversations with the agent can be stored and used for an extended period of time. For example, a support agent in customer service can commission a service and also propose a suitable alternative service.

Autonomous control of reports, workflows, and task management – without manual handovers.

Use case:
A financial services provider uses AI agents for process management. The agents generate monthly reports from various data sources, suggest corrections, and coordinate the approval process independently. This minimizes errors and delivers reports much faster.

Independent execution of simulation series, experimental planning, and evaluation of complex data sets.

Use case:
A pharmaceutical company uses agentic AI to independently perform drug interaction simulations, vary different parameters, and propose the most promising candidates for clinical trials. This significantly accelerates the innovation cycle.

Intelligent wizards with long-term memory retain contexts and react predictively.

Use case:
A telecommunications provider integrates an AI agent into the existing chat, one that not only answers customer requests but learns from past interactions, proactively offers problem solutions, and recommends individual service packages. As a result, customer satisfaction increases measurably.

System monitoring, troubleshooting, and independent execution of countermeasures.

Use case:
A cloud service provider uses an AI agent to monitor its infrastructure; the agent analyzes, prioritizes, and automates remediation measures such as restarts and load balancing – around the clock, with minimal human supervision.

Adaptive learning guides adapt dynamically to the level of knowledge and learning objectives.

Use case:
An online learning platform uses agentic AI to create personalized learning plans based on users’ progress, interests, and weaknesses. The agent dynamically adapts content and motivates users with interactive challenges; this significantly improves learning outcomes.

Assistance systems that evaluate medical data and support treatment processes.

Use case:
A hospital uses agentic AI to optimize patients’ treatment plans. The agent analyzes laboratory values and clinical guidelines, suggests therapies, and dynamically adapts them to new findings. This relieves the burden on the staff and improves care.

Automated systems analyze claims reports, review contract details, and independently assist in making decisions relevant to regulation.

Use case:
An insurer uses an AI agent to assess incoming claims automatically. The agent reviews the terms of the contract, identifies potential fraud, and makes individual regulatory proposals. This drastically reduces processing times and increases customer satisfaction.

Agentic AI takes regulatory requirements into account and prepares clinical documentation automatically.

Use case:
A pharmaceutical company uses agentic AI to automate the creation of submission documents for regulatory agencies (e.g., EMA, FDA). The agent searches study data, extracts relevant results, creates designs for modules such as the clinical study report and then adapts these so they are compliant. This accelerates the approval process and significantly reduces manual documentation effort.

AI agents monitor production processes, detect errors, and proactively initiate maintenance measures.

Use case:
In a production plant, an agent continuously monitors machine states and process data. It detects irregularities at an early stage, automatically plans maintenance intervals, and makes adjustments to avoid errors. This reduces downtime and increases product quality for the long term.