AI Agents – How companies benefit from the AI trend “Agentic AI”
AI agents are currently listed as one of the biggest AI trends – and rightly so: they are the next level of automation and can react intelligently, adaptively and autonomously. They represent a new generation of intelligent systems and help to scale processes not only more efficiently, but also more intelligently. In this article, you will learn how AI agents work, which fields of application are available and how you can strategically exploit the potential of this technology together with Ventum Consulting.
What are AI agents?
AI agents are autonomous, digital assistants that independently collect and analyze information and translate it into specific actions. In contrast to traditional, rule-based automation solutions, they work in a context-related and adaptive manner: they observe their environment, evaluate data from various sources and react flexibly to new requirements – always with a defined goal in mind.
In the corporate environment, AI agents take on tasks along entire process chains. They recognize patterns in data streams, provide well-founded recommendations, control applications via interfaces and continuously optimize their behaviour based on feedback. This results in systems that are not only efficient, but also adaptive and scalable – with noticeable added value for productivity and data-based decision-making quality.
Not every form of artificial intelligence acts independently. Many AI applications, such as classic machine learning models or language models (LLMs), work within clearly defined boundaries and require human guidance. AI agents are fundamentally different: they are designed to pursue goals, make decisions and carry out tasks independently.
The following overview shows key differences between AI agents and other common types of AI:
AI agents differ not only in their technical design, but also in their role and objectives within a process. Depending on the area of application, degree of autonomy and reaction behavior, different types of agents can be distinguished:
- Reactive AI agents
These agents react to predefined triggers without long-term planning or context storage. They are well suited to clearly structured, recurring tasks – such as monitoring or simple decision-making processes. Their strength lies in their speed and reliability within defined framework conditions.
Example:
An agent that detects temperature deviations in a production line and automatically sends a warning message. - Proactive AI agents
Proactive agents do not just act reactively, but actively pursue a goal. They analyze situations independently, anticipate developments and initiate measures before a problem arises. They are ideal for dynamic environments where adaptability is required.
Example:
An agent that independently takes care of reordering materials before a bottleneck occurs – based on demand forecasts and stock data. - Multi-agent systems
In multi-agent systems, several specialized AI agents work together on complex tasks. They communicate with each other, share information and coordinate their actions – often with clearly assigned roles. The result is a flexible, scalable system with high efficiency.
Example:
A customer service system in which one agent classifies incoming inquiries, a second prepares the appropriate responses and a third handles communication with the CRM.
How AI agents work
AI agents start by collecting information via interfaces to internal systems, user interactions, external data sources or sensors. This creates a large information base that represents the context of a situation and is essential for subsequent decisions.
Examples of data sources:
- API access to ERP, CRM or support systems
- E-mail inboxes or chat histories
- Sensor data or log files
- Input by users
Based on the information collected, the agent analyzes the current situation. Depending on the goal, role or logic, it makes autonomous decisions:
- Goal-oriented: Which action brings the agent closer to the defined goal?
- Role-based: Which tasks fall within his area of responsibility
- Context-dependent: What was previously recognized, stored or learned
- Logic-oriented: Which rules or decision-making structures apply?
This phase distinguishes simple automation from genuine intelligent process control.
Once the decision has been made, the agent initiates specific steps – independently or by transferring them to a connected system. It actively interacts with its environment and triggers processes, generates content or delegates tasks to other agents.
Typical actions:
- Creation of a ticket in the service system
- Sending automated reply emails
- Updating data in various systems
- Passing on tasks to specialized sub-agents
Feedback mechanisms also allow the agent to continuously adapt and improve its behavior.
The advantages of AI agents
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Examples & use cases from practice
For Ventum Consulting, AI agents have long been more than just an experiment – they are part of real, productive business processes. The following examples show how we have already successfully implemented AI agents.
The possible applications in practice are almost limitless – provided that use cases are clearly defined, evaluated in terms of their business impact and implemented consistently. This is where the challenge often lies: not in the technology or the integration, but in having the courage to think of innovative scenarios and test them for the greatest possible impact.
Industry: Wholesale
Core task: Automatic recognition, reading and structuring of complaints incl. SAP ticket creation
The role of the AI agent:
- Monitoring of incoming e-mails
- Contextual analysis of PDF attachments
- Structured transfer to Action Engine
- SAP integration for ticket creation and data enrichment
Impact:
- Time saving
- Low error rate
- Faster processing without manual intervention
Area: Customer Service & Order Processing
Core task: Intelligent analysis and answering of incoming customer inquiries regarding delivery status
The role of the AI agent:
- Context-based categorization & decomposition of mixed requests
- Linking with internal systems (e.g. production status, tracking)
- Automatic dispatch of personalized reply e-mails
Impact:
- Higher service speed
- Relief for customer service
- Increased customer satisfaction
- Transparency through analysis dashboards
Industry: Product development
Core task: Timely migration and archiving of specifications after system changeover
The role of the AI agent:
- Automatic identification, conversion and filing of relevant documents
- Combination of RPA and AI agent for data processing
- Audit-proof archiving in human-readable format
Impact:
- Minimization of manual effort
- Revision security
- Process reliability with high data volumes
- Reduction of operational risks and costs
Recognizing and implementing potential for AI agents
AI agents can be used in almost countless areas of a company – but that doesn’t mean that the greatest benefit is immediately generated everywhere. The key lies not in a long list of possible applications, but in the targeted selection of strategically relevant use cases that create real added value: measurable, scalable and practical.
What sounds simple is often complex in reality – because it requires clarity about goals, processes, data flows and responsibilities. This is precisely why we at Ventum Consulting rely on a structured analysis of potential: together we identify the most sensible entry point, evaluate technical feasibility and economic impact – and thus create the basis for successful implementation.
The following examples give an impression of where AI agents play a role today – and where potential may lie.
- Automated returns processing
- Intelligent product recommendations
- Stock monitoring and automatic reordering
- Process monitoring and incident analysis
- Coordination of maintenance & predictive maintenance
- Production planning based on demand forecasts
- Automated appointment allocation & patient communication
- Medication and warehouse management
- Support with monitoring or documentation
- Claims processing with preliminary review
- Automated compliance checks
- Contract evaluation and classification
- Real-time tracking and exception handling
- Automated freight booking & delivery confirmation
- Analysis of supply bottlenecks & alternatives
Conclusion: AI agents are revolutionizing business processes
AI agents are more than just a technological trend – they are a crucial building block for the next level of intelligent process automation. Their potential lies not only in increasing efficiency, but above all in their ability to act, learn and continuously develop based on context.
The practical examples show: The successful use of AI agents is not a vision of the future, but already a reality – if technology, business processes and objectives are consistently thought through together. The decisive factor here is not to know every possible use case, but to identify the right use cases, evaluate them economically and implement them with a clear goal in mind.
This is exactly where we at Ventum Consulting come in: With a methodically sound approach, a deep understanding of technology and an eye for the essentials, we support companies in the development, implementation and scaling of AI agents – from the first pilot to productive integration in the core business.
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- Experience:20 years of expertise in process optimization
- Individual solutions: Customized strategies with added value.
- Flexibility: Adaptation to your processes and systems
- End-to-end service: complete support from analysis to implementation
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