About the author
Henry Ohl
Consultant
How multi-agent systems turn AI from a single model into a team
Artificial intelligence is currently developing faster than any other technology, and the way we work with it is changing fundamentally. Until a few years ago, the focus was primarily on large language models (LLMs). Today, we are at a point where these models are no longer the sole focus, but are becoming building blocks in a larger system: Multi-agent systems. While classic AI applications perform a task on demand, multi-agent systems go a decisive step further. They consist of several specialised agents, analyse goals, coordinate tasks independently, carry out sub-processes, check each other and refine results step by step. In short, they work like a digital team.
About the author
Henry Ohl
Consultant
What makes multi-agent systems so special?
Although a single AI model can take on many tasks, it quickly reaches its limits when it comes to more complex requirements. This is precisely where multi-agent systems come in. Instead of relying on an all-rounder, their structure follows the principle of „Division of labour instead of jack of all trades“: Several specialised agents take on different roles, analyse goals, coordinate tasks and check each other's results. This clear specialisation significantly increases the quality and reliability of the results - for example through the following roles:
Planner / Coordinator
Breaks down a goal into meaningful steps
Research agent
Collects and evaluates information
Analysis agent
Checks data, draws conclusions
Executing agent
Creates content, executes processes
Quality agent
Monitors quality & consistency
Compliance agent
Checks rules, guidelines & risks
Why this will be relevant in 2026
The phase of pure prompting - i.e. „do this, do that“ - is slowly coming to an end. Many companies are realising that the next productivity boost will only come when AI can do more than just formulate answers. It needs to understand tasks, plan processes, execute work steps, check results and continuously improve them. As a result, AI is developing from a pure creative assistant into a active labour force within a workflow. For companies, this means one thing above all: processes can no longer just be supported selectively, but can be fully automated.
How multi-agent systems work - simply explained
Imagine a company wants to carry out a competitive analysis for a new product. In the past, we would have asked an LLM to draw up a list that would have been laborious to rework. A multi-agent system works differently. The result is consistent, thorough and often completed faster than a team could do it manually.
Define goal
„Create a complete competitive analysis of our new product segment.“
Planner agent
Breaks down the task: identify the market, research competitors, compare prices, analyse features, evaluate opportunities/risks.
Research agents (parallel)
Search various sources, prepare information.
Analysis agent
Summarises data, evaluates trends, identifies patterns.
Content agent
Formulates the final analysis, creates presentations or reports.
Quality and compliance agents
Remove errors, check contradictions, pay attention to company rules.
Why this development is so important for companies
For companies, the use of AI is no longer just about completing individual tasks faster. The decisive factor is how well entire processes can be supported or automated. This is precisely where multi-agent systems show their potential: the interaction of several specialised agents allows tasks to be distributed more efficiently, results to be checked more effectively and processes to be expanded more flexibly. This results in the following for companies Several key advantages:
speed
Many sub-processes run in parallel instead of one after the other
Quality & reliability
Built-in control mechanisms reduce errors, hallucinations and inconsistencies.
Scalability
New agents can be added or specialised quickly without having to redevelop entire workflows.
What does this mean for us at LM IT?
At LM, we bundle expertise, practical experience and real-life use cases in strategic projects. Multi-agent systems are an integral part of our AI expertise and form the foundation for innovative services, products and consulting offerings. We systematically expand our internal expertise, identify specific added value and actively support our customers on their AI journey. At the same time, we ensure that AI-supported solutions are anchored in our portfolio for the long term. Our focus is on developing a deep understanding of how AI can be specifically orchestrated and seamlessly integrated into real business processes.
Conclusion
2026 is a key year for the transformation of AI: away from individual solutions and towards automated, orchestrated AI systems that act like digital teams. Multi-agent systems open up new opportunities for companies to speed up complex processes, reduce errors and implement innovations more quickly.
We are at the beginning of a phase in which AI is no longer just providing support, it is working with us. And that is precisely why it is now so important to understand these technologies, try them out and develop them further together.
My personal motivation
In my spare time, I started working intensively with creative AI tools early on, from music and images to more complex workflows. What began as private experimentation showed me how enormously versatile AI can be when used for more than just content creation.