bbv KI Webinar - Swarm Intelligence

In our webinar on "Swarm Intelligence: How AI Agents Transform Your Organisation" I explored together with Joel Barmettler how organisations can concretely benefit from generative AI and why now is the right moment to get started. Our aim was not only to question the current AI hype but above all to show that AI has long since become an effective tool in everyday business operations. Many organisations have already recognised that artificial intelligence can be used to automate processes and open up new business areas.

"AI agents bring real value to organisations when you define tasks clearly and give them the right tools."

Through this focus on the practical benefit of AI we created a framework in which questions of feasibility, security and cost control become clear. Especially in the current phase, in which generative AI is advancing at a rapid pace, it makes sense to plan the entry deliberately and link it to a clear strategy.

The Path to Generative AI In the next step we looked at the recent research history to show how language models have developed from GPT-2 through GPT-3 (which many know through ChatGPT) to the multi-agent systems currently being discussed. This development is remarkable because it makes clear why AI agents can achieve far more today than just a few years ago.

The concept of few-shot learning was presented, for instance. This concerns how a large language model like GPT-3 can learn new tasks without being completely retrained each time. It is sufficient to give the model a few examples for it to recognise patterns and transfer them independently. Even more interesting are the so-called ReAct approaches, in which language models are not limited to pure text comprehension but can additionally "think" and "tap into" external tools or data sources to handle complex tasks.

"The ability of AI models to independently 'think things through' and act appropriately opens up entirely new possibilities when it comes to automating high-level tasks."

This means we are able to implement not only simple question-and-answer scenarios but to create agents that independently plan intermediate steps. They fetch the necessary information themselves, for example from internal company documents, or access external services to complete their task.

From Individual Agents to the AI Swarm At this point we made the connection to the idea of deploying AI agents not in isolation but networked with one another. This creates a form of swarm intelligence where each agent is responsible for a clearly defined task while simultaneously having the ability to cooperate with other agents or coordinate with them when needed. The result is that an organisation's performance can be effectively multiplied because several specialised agents work in parallel or in coordination.

"A single agent can already be helpful, but the real leverage comes when multiple agents act in teams, mutually controlling and complementing each other."

We particularly highlighted two basic interaction models. In the first model, the group chat, all agents (and human team members when needed) exchange directly with one another. This resembles the chat groups we already use in everyday working life in Slack or Teams. In the second model there is a coordinator agent that controls all communication and reports results back in a concise, easily understandable form. Which variant is better suited depends on factors such as team culture, working style and specific project requirements.

Implementation and Practice Of course we also wanted to show how an organisation can harness this potential. In the webinar we presented four consecutive phases that have proven themselves in practice for successfully implementing AI agents step by step.

In the introduction phase, the first step is to assign initial AI agents to relatively narrow, well-definable tasks. This allows both employees and the AI to learn what works and where potential limits lie. This approach builds mutual trust and allows risks to be kept within a controlled framework.

Once initial successes have been achieved, the second phase can expand the autonomy of the agents. The focus here is above all on agents independently recognising knowledge gaps and actively asking experts for new information when something is missing. This process requires some patience from the workforce at times, but sustainably strengthens the agents' self-understanding in day-to-day business.

In the third phase, targeted networking takes place. Here several agents are configured so that they can work on joint projects. For the organisation this means a noticeable efficiency boost, as the agents steer each other to a degree in order to bring in their respective areas of expertise.

Once this collaboration is well established, the next step can be taken in the fourth phase and a superordinate coordinator agent installed. This accelerates complex projects in particular, because no longer does every team member have to understand and select every agent. Instead there is a central point of contact that internally coordinates all required resources, both human and machine.

"The great difference between a single agent and a swarm lies in the decentralised self-organisation and in the interplay of specialised AI systems."

As already mentioned, success in this implementation phase depends heavily on security and data protection aspects. We therefore also presented concrete approaches for controlling permissions, ensuring cost transparency and continuously monitoring the quality of agents.

Outlook In the final part of the webinar we envisioned how this interplay can be thought of on an even larger scale. In the near future, companies will not only have their internal AI agents but will simultaneously be able to cooperate with external agents from other organisations. This creates a true AI ecosystem in which specialised agents are offered "as a service". The idea that entire companies base themselves on developing and providing AI agents for others is no longer futuristic: the open-source community is already working feverishly on building appropriate multi-agent frameworks.

"We will soon see platforms on which companies can buy or rent AI agents from different providers to get tailored solutions for their individual challenges."

The subsequent question round showed that the demand for information in the areas of data protection, on-premise solutions, rights management and cost optimisation is very high. Our conclusion is that it is essential for organisations to define clear processes and a technology stack from the outset, so that the use of AI agents is not only secure but also economically viable.

We look forward to exploring these topics further in the next webinar in our series, particularly the aspect of shared knowledge management between humans and machines. The potential to capture, structure and make company knowledge usable more efficiently is greater than ever with AI agents.

"Those who start now have the chance to actively shape the design of these new value chains and secure a decisive competitive advantage in the long run."

We are convinced that AI agents can become a "game changer" when they are anchored strategically in an organisation and trust, transparency and appropriate processes are considered in good time.