Digitalisation & Technology, 5 November 2025
Multi-Agent-AI
When Artificial Colleagues Become Digital Teams
Artificial intelligence has already cemented its role in countless digital business sectors. The arrival of agentic AI ramped up automation, but it’s the emergence of multi-agent systems that truly unlocks the potential of AI. Instead of solitary digital assistants handling basic tasks, we now have teams of AI agents collaborating to tackle complex business processes.
The buzz around generative AI isn’t fading. After an explosion of specialist AI tools for specific jobs, we saw the rise of agentic AI: digital colleagues able to autonomously perform tasks within set boundaries. Now, the next frontier is multi-agentic AI – where multiple AI agents join forces to manage intricate projects that would overwhelm a single tool.
From Solo Agents to Collaborative Teams
Multi-agent systems are set to be one of 2025’s biggest tech trends. They mark a shift from AI solutions designed for narrow tasks to collaborative teams capable of handling nuanced, multifaceted processes. Once configured, these systems run automatically and autonomously – just as the technology promises.
The key difference? Collaboration. Just as a successful human team shares information, makes independent decisions, and improves outcomes through communication, multi-agent AI systems do the same. Each agent brings unique capabilities, working in tandem to achieve shared objectives. Sounds familiar, doesn’t it? It’s the digital mirror of a high-performing team in the office.
AI agents are rapidly becoming a way to introduce LLMs on a large scale in the insurance industry.
Phase 1: Simple AI Tools (2022-2023)
- Isolated AI chat interactions
- No memory between sessions
- User inputs, AI outputs
- Examples: text generation, image creation, code snippets, translation
Phase 2: AI Agents (2023-2024)
- Autonomous multi-step actions
- Persistent memory and context
- Web searches, API calls, code execution
- Examples: AI co-pilots, desktop assistants
Phase 3: Multi-agent Systems (2024 onwards)
- Multiple specialist agents collaborating
- Parallel task execution
- Inter-agent communication and coordination
- Examples: CrewAI, AutoGPT, MetaGPT
Where Multi-agent Systems Shine
Let’s explore some real-world applications where multi-agent AI teams are making an impact:
Software Development & Programming
Software projects naturally break down into distinct stages, making them ideal for multi-agent systems. Instead of a linear workflow, AI agents can tackle planning, coding, testing, debugging, documentation, and deployment in parallel. Imagine:
- Agent 1 reviews code quality,
- Agent 2 hunts for security vulnerabilities,
- Agent 3 benchmarks performance.
The result? Faster, more robust development cycles. It’s no wonder much of today’s AI code is written by AI itself.
Supply Chain Optimisation
AI agents can assume the roles of suppliers, logistics firms, and manufacturers. They negotiate, adapt production to demand forecasts, and orchestrate deliveries. For instance:
- Agent 1 analyses demand using sales history and external data (weather, holidays, competitor activity, even social sentiment).
- Agent 2 manages inventory and orders, optimising stock levels and costs.
- Agent 3 oversees logistics, factoring in traffic, fuel prices, and capacity at ports or airports.
- Agent 4 scans for disruptions – from extreme weather to strikes – and triggers real-time adjustments.
Market Research
Market research is a natural fit for multi-agent teams. It’s all about pattern recognition at scale:
- Agent 1 gathers raw data from social media, news, and industry reports.
- Agent 2 spots trends, correlations, and market gaps.
- Agent 3 turns insights into actionable recommendations.
Unlocking New Possibilities
Human oversight remains vital – monitoring, maintaining, and fine-tuning these systems. The time saved can be reinvested, combining AI results with human expertise for even better outcomes. Some sectors, however, demand more than humans alone can deliver. Take energy: managing grids in real-time, round-the-clock, requires AI’s speed and adaptability. The same goes for cloud infrastructure and traffic management in sprawling cities.
Agents are the key to creating economic value through GenAI.
Coordinating swarms of AI agents isn’t trivial. Secure, reliable communication is essential, and standards for interoperability are still evolving. There are open questions around liability and compliance – especially under the EU AI Act and UK GDPR. Who’s responsible if something goes wrong? These issues will be at the forefront as adoption grows.
Is It Really That Simple?
The user-friendly nature of chat-based large language models has set sky-high expectations for all AI tools. Slick marketing makes multi-agent systems sound as easy as building with Lego. But, as Markus Sekulla’s self-test revealed, the more powerful these systems become, the more technical expertise is needed to set them up.
Still, it may only be a matter of time before building an AI team is as straightforward as assembling a human one. Platforms like n8n are already experimenting with natural language interfaces to create agent systems based on user ideas.
Text: Falk Hedemann
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