Fraunhofer Institute for Material Flow and Logistics IMLTo optimize logistics processes, the Fraunhofer IML combines mathematical optimization with practical assistance systems. Our institute analyzes big data from transportation, warehousing, and production and uses it to formulate precise optimization models. Algorithms calculate robust scenarios for sequencing, routing, order quantities, and resource allocation. Graphical user interfaces present the results in an easy-to-understand format and support the day-to-day decision-making of logistics planners. Agile implementation and continuous optimization ensure the sustainable impact of the models.
Agentic AI
Agentic AI in logistics
Logistics companies face significant challenges due to skilled labour shortages as well as cost and competitive pressures. Agentic AI combines generative AI with autonomous software agents, extracts information from unstructured data, automates recurring processes, and enhances data-driven decision-making. Our research focuses on the effective convergence of generative AI, robotic process automation, and optimization methods in production and logistics.
Concept, context, and challenges
Agentic AI describes autonomous, goal-oriented software agents that prepare decisions based on generative models and initiate processes in real-time. In logistics, this approach addresses the increasing complexity of available data, the decisions to be made, monotonous and repetitive processes, as well as growing demands on response times.
Companies require scalable solutions that connect heterogeneous data sources (from ERP, WMS, MES), capture current and forecasted situations (e.g., bottlenecks), and prepare and make decisions (e.g., resource allocation). Fraunhofer IML investigates how agent architectures can be combined with generative AI and classical optimization methods to effectively support planning and control in logistics. In our research and industry projects, we develop reference architectures, evaluate use cases, and support executives in prioritizing investments in agentic AI-based systems.
Your entry into Agentic AI
Use our research on Agentic AI for your logistics projects! Do you want to evaluate which agentic AI use cases are worthwhile for your company and how to plan your entry in a structured manner? We analyze your data situation, system landscape, and investment needs together with you and develop a tailored roadmap!
Find out about the possibilities for your company!
Implementing solutions with Agentic AI
Our services encompass business and technical expertise in Agentic AI across all logistics areas:
- Potential analyses for agentic AI applications
- Requirements analysis and evaluation of agent architectures and orchestration platforms for new agentic AI solutions
- Development of a vendor-neutral decision-making basis for the selection of agentic AI solutions
- Development of business concepts for multi-agent systems facilitating collaboration between humans, AI agents, and existing IT systems
- Business support for projects integrating agentic AI into existing ERP, WMS, and TMS landscapes
- Concept and implementation of customized agentic AI solutions for dispatching, planning, purchasing, and sales processes
- Agentic AI solutions benefit all supply chain actors through faster decisions, more robust networks, and more efficient end-to-end processes.
Agentic AI in information logistics
Information logistics represents an ideal application area for agentic AI, as its core competencies are in high demand here: independently finding, evaluating, processing, and providing information at the right time and place. This opens up applications in knowledge and document management, bureaucracy reduction, and regulatory compliance that previously had very limited support from IT systems.
Below are exemplary application possibilities of Agentic AI in information logistics:
| Knowledge and document management | Proactive support | Bureaucracy reduction |
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| Purchasing and sales | Regulatory affairs and compliance | |
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"Autonomous agents only provide added value to executives if organizations clearly define responsibilities, data quality, and decision rights, and systematically design human-machine interaction."
Benjamin Korth, Head of Department Information Logistics
Robotic process automation for transparent supply chains
Robotic Process Automation (RPA) refers to the automation of rule-based, recurring tasks. In contrast to Agentic AI, the steps to be performed must be known, and data must be structured. Typical tasks include routine processes, data transfers, form processing, or reporting. These are taken over by so-called software robots that, for example, aggregate and process data from ERP, WMS, TMS, or MES systems. This reduces transfer errors, relieves employees from monotonous click processes, and ensures audit trails are documented seamlessly.
RPA represents a low-threshold entry point for automating information processes. Our experts support you in replacing Excel workarounds, defining processing rules, and realizing the integration of RPA into your corporate IT.
Agentic AI: Our references
Industrie 4.0 RechtTestbed
The joint project Industrie 4.0 RechtTestbed aims to develop a digital experimental field to legally test automated Industrie 4.0 business and contract processes. In this way, we are developing standards for smart contracts for legally compliant and autonomous production chains.
Use Agentic AI efficiently!
FAQ: Agentic AI
What exactly is agentic AI?
Agentic AI systems independently pursue goals, make decisions, carry out actions, and adapt to feedback. They combine observation, planning, tool use, and cooperation among multiple agents to solve complex tasks. Difference from generative AI: It translates content into actions in a goal-oriented manner.
Is ChatGPT an agentic AI?
In its basic form (chat window, text-only responses), ChatGPT is primarily generative AI, not a full-fledged agentic system. It responds to inputs but does not pursue its own long-term goals or control its environment.
However, if ChatGPT is embedded in a framework that provides it with tools, APIs, storage, and workflows, it can become part of an agent-based AI that plans tasks, uses tools, and initiates processes.
What is the difference between generative AI and agent-based AI?
Generative AI generates content such as text, images, or code on demand. Agent-based AI implements this content by planning goals, utilizing memory and feedback, often with generative AI as a module. Generative AI is thus a building block, while agent-based AI is a complete system of action.
What are the 4 types of agentic AI?
1. Assistance Agents: Support users (e.g., writing text, answering questions).
2. Process/Workflow Agents: Automate processes (e.g., forms).
3. Research Agents: Analyze data autonomously.
4. Orchestration Agents: Coordinate multiple agents for complex processes.
How does Agentic AI differ from traditional automation?
Traditional automation follows fixed rules and workflows. Agentic AI uses flexible agents that take goals, context, and uncertainty into account. They adapt their decisions to new data and work together. When combined with Robotic Process Automation, agents can delegate rule-based tasks and focus on planning, exception handling, and coordination.