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TRUSTEQ | Corporative Excellence
AI Multi-Agent Systems

The Future of Automation

AI agents are the next big revolution in the field of artificial intelligence. At their core, these agents are advanced AI models that have the ability to use tools in a targeted way. However, their true potential is unleashed when multiple agents cooperate and interact with each other. What new opportunities does this open up for businesses and organizations?

Imagine an ant colony: each ant seems to act autonomously, performing simple tasks and interacting with other ants. Yet, together they achieve impressive results—from foraging for food to building stable structures and defending against threats. This concept illustrates what is known in computer science as a multi-agent system: a network of independent agents that interact with each other and can collectively solve complex tasks.

Ant colonies are a great example of multi-agent systems because they demonstrate how individual actors in a system can achieve a goal through communication and cooperation that far exceeds the capabilities of the individual agents. This concept can also be applied to human-made technical systems. When artificial intelligence is used as the technology, this is referred to as AI agents.

AI Meets Multi-Agent Systems: A New Dimension

Multi-agent systems are by no means a completely new phenomenon—they are already used in numerous application areas today.

Traditional multi-agent systems are often rule-based or rely on algorithms and heuristics without AI technologies. Examples of these multi-agent systems include:

AI technologies open up new possibilities beyond traditional multi-agent systems. In an AI multi-agent system, the agents, unlike the previously mentioned examples, not only act autonomously but can also take on tasks that previously required human involvement through the use of AI models. Examples include independent text generation for answering customer inquiries, decision-making based on text data, or the analysis and interpretation of image data, such as in medical diagnostics. The language and multimodal models released in recent years by OpenAI, Google, or Anthropic, however, offer many more potential applications.

AI Agents: Characteristics and Potential

Large language and multimodal models can not only answer questions or generate texts, but they also serve as the "language center" of AI agents, enabling them to interact with humans or other agents. The following characteristics of the models, or their established chat applications (e.g., ChatGPT), are particularly relevant for AI agents:

AI visualisation
Multi Agent Systems Graphic

Additional Capabilities

However, an AI agent requires more than just a powerful language model. To operate effectively in an AI multi-agent system, it must possess additional capabilities:

Capabilities of AI visualised

Technologies and Frameworks

New technologies and frameworks such as LangGraph, AutoGen, or CrewAI enable AI designers and developers to instantiate common language and multimodal AI models as agents and tailor their properties and behavior to specific use cases.

Practical Examples: AI Agents in Action

AI agents are already being used in practice today. For instance, the Swedish payment provider Klarna has been utilizing AI agents in customer service since 2024. These agents can handle simpler customer requests directly while supporting human service representatives with more complex inquiries, thereby increasing efficiency potential [1]

The goal: significant cost savings while improving customer satisfaction.

In a future scenario, AI multi-agent systems could take over various process steps in insurance claims handling:

A customer interaction agent would receive reports in natural language and capture all relevant information. A coordination agent would delegate subtasks to other agents: a contract analysis agent could review insurance terms and coverage, while a document verification agent could analyze the plausibility of submitted information and documents. A decision-making agent would assess whether an expert should be involved or if compensation can be processed directly.

The collaboration between AI agents enables fast and transparent processing, aiming for cost savings and improved customer satisfaction, similar to the Klarna example.

The Multi-Agent Approach: Key Advantages

Compared to a single agent handling all roles, the multi-agent approach offers significant benefits:

1) Responsibilities for individual agents can be distributed across different departments or even organizations

2) Independent (further) development of individual agents—potentially with different solution architectures or technologies.

3) Ease of expanding the system with additional capabilities.



Communication Between Agents: Transparent and Powerful

The question of how AI agents communicate with each other has a surprisingly simple answer: through natural language.

Unlike most IT systems, which rely on technically complex and often difficult-to-read protocols, communication between AI agents is inherently understandable and transparent. This facilitates coordination between agents following familiar behavior patterns (e.g., hierarchical or majority-based decisions) and allows human stakeholders to comprehend the agents' decision-making processes.

Additionally, AI multi-agent systems that utilize state-of-the-art language models are generally more powerful than individual models or single agents using the same models. This enhances their overall performance, enables new use cases, and provides users with significant competitive advantages.

A Glimpse into the Future: AI Multi-Agent Systems and Their Implementation

AI multi-agent systems significantly expand the potential of automation. Imagine a team of specialized AI agents supporting or even independently managing processes within your company.

Technologies such as Azure AI Services, AWS AI Services, or similar cloud-based tools offer straightforward integration options for embedding AI agents into existing IT infrastructures. For use cases that demand maximum data security, a fully on-premises implementation of AI agents and multi-agent systems is, of course, also possible.


Implementation in Your Company: The journey to AI-agents

The introduction of AI multi-agent systems requires a strategic approach:

TRUSTEQ supports you as a trusted advisor at every stage: from identifying suitable use cases to their piloting and implementation, all the way to ensuring regulatory compliance—including the requirements of the EU AI Act.

AI Multi-Agent Systems as a Game Changer

AI multi-agent systems represent a turning point in automation. They offer companies the opportunity to make processes more efficient, cost-effective, and scalable.

IT decision-makers should keep a close eye on this trend—not only to enhance efficiency but also to ensure long-term competitiveness.

Dr. Lukas BonenbergerSenior AI Consultant bei TRUSTEQ