The investment is worthwhile because AI agent systems offer many advantages. In contrast to simple prompts in tools such as ChatGPT, which provide quick but often superficial responses, multi-agent systems can handle complex, multi-stage workflows without constant human intervention.

These multi-agent systems work completely autonomously: they exchange information independently in the background, use tools tailored to their task, and constantly monitor and improve the result. In this solution process, a coordinating agent ensures that each aspect is handled by the most suitable specialist agent. As a result, the agentic workflow enables relevant processes in marketing and sales to be automated far more effectively than conventional, one-step interactions with AI systems.

An example: A customer books a flight to New York. Now, a multi-agent system from the airline is asked to create a personalized follow-up email for her. The customer data agent provides the team with existing insights. The research expert, who is a master of internet searches, finds out that a big Christmas shopping event is taking place in New York at this time. He also searches for suitable shopping tips, hotels and events that are available at this time of year. The collected information is sent to the writing assistant, who then composes a brand and customer-oriented text. The e-mail is checked by the brand safety agent. And the coordination agent controls the group chat between the individual specialists. Through iteration cycles, the AI team finally arrives at a highly personalized follow-up email to the customer.

7 Reasons For Leveraging Multi-Agent Systems
 

1. Quality assurance

Simple AI systems often provide generic or partially incorrect answers, which is why employees have to check and rework the results. This is neither effective nor efficient. Multi-agent systems that have brand safety and bias agents, compliance and data protection agents in the team, for example, ensure quality and edit themselves.

2. Scalability

AI agent teams can complete many tasks in parallel. This speeds up processes and reduces bottlenecks while managers are able to scale tasks and projects without increasing staff.

3. Flexibility

The flexible use of large and small language models, which have different strengths and weaknesses, allow for flexibility in optimizing costs, quality and speed of the entire system.

4. Specialization

Different agents in the team use different models, data sources and tools. The models they use can be explicitly optimized for their purpose. In principle, the agents combine their strengths and solve even complex tasks much more efficiently.

5. Personalization

AI agents enable highly personalized customer experiences. This is because they analyze their behavior, preferences and feedback and generate individual content in real-time. This enables real scaling compared to rule-based systems with pre-produced content modules.

6. Adaptive learning ability

AI agents have a short-term and a long-term memory. This means they learn with every task and are constantly improving. This adaptation to current customer needs or market developments makes agent systems extremely effective, agile, responsive and future-proof.

7. Cost efficiency

By automating tasks with multi-agent systems, brands can reduce their operating costs. In addition, the agentic workflow makes extremely efficient use of existing resources, improving overall cost-effectiveness.

Multi-agency systems are becoming irreplaceable in modern marketing and brand management: they not only improve quality and efficiency, but also enable personalized customer experiences, fast reactions to market changes and low costs. Those who rely on agentic workflows today will secure a long-term competitive advantage in the digital world.

Author: Sebastian Küpers, Chief Transformation Officer, Plan.Net Group