🦸🏻#18: Why Single AI Isn't Enough? How Multi-Agent Systems Redefine AI's Potential

This sentiment beautifully captures the essence of Multi-Agent Systems (MAS). A single AI model can do a lot – but sometimes, a network of simpler agents working together can do more. From coordinating delivery drone fleets to optimizing smart city grids or simulating global markets, MAS involve multiple autonomous agents collaborating, competing, or coexisting to tackle problems beyond the reach of any single entity.

Unlike monolithic AI systems built for centralized tasks, MAS thrive on decentralization! Each agent has its own perspective, goals, and capabilities. Their interactions drive sophisticated, adaptive, and often emergent behaviors. As our world grows interconnected, addressing challenges like global logistics or climate change, MAS principles are becoming essential. But building MAS is not easy. Getting agents to coordinate well, without causing chaos, is a real challenge. How do you make sure a swarm of robots explores a collapsed building without bumping into each other? How do you stop trading agents from triggering a market crash?

In this piece, we walk through MAS step by step: we'll trace the origins of MAS, explore their core components, how they work in practice, where they’re being used, and what’s next. Let’s begin.

What’s in today’s episode?

• From DAI to MAS: A little bit of history

• Core Components of MAS

• Architectures and Organizational Structures in MAS

• Types of Multi-Agent Systems

• Coordinating Chaos: Swarm Robotics in Disaster Response

• Recent Research Developments and Trends (2023–2025)

• Concluding Thoughts: The Future of Collective Intelligence and MAS

• Resources to dive deeper

From DAI to MAS: A little bit of history

Multi-Agent Systems aren’t new. Their story goes back to the late 1970s and early 1980s, when researchers started to work on Distributed Artificial Intelligence, or DAI. They were looking for ways to handle problems that were too big or complex for a single AI system. Some knowledge was naturally spread out across different sources, and working in parallel promised better performance. So early DAI efforts focused on how to divide up a problem and spread the reasoning across multiple parts – called “knowledge sources” or “nodes.”

Some early efforts borrowed from classic AI. Victor Lesser’s Distributed Vehicle Monitoring Testbed (DVMT) used a blackboard architecture to let distributed agents interpret sensor data together. Around the same time, Carl Hewitt’s Actor model introduced a system of independent “actors” that talked to each other via messages – a big step toward agents that could operate concurrently. Marvin Minsky’s Society of Mind (1986) also helped set the tone, imagining the mind itself as a community of smaller interacting parts.

While there isn't one single person credited with coining the term "Multi-Agent Systems," it was a collective evolution within the DAI field, solidifying as a distinct research area with its current name in the mid-1990s. Researchers like Victor Lesser, Les Gasser, Michael Wooldridge, and Nick Jennings played key roles in shaping this new way of thinking. While MAS grew out of DAI, it brought a shift in focus: the individual components were no longer just pieces of a bigger machine. These were agents – each with its own goals, skills, and decision-making ability. They didn’t need constant direction. They could act on their own, interact with others, and work things out locally.

One early and important milestone was Reid G. Smith’s Contract Net Protocol, introduced in 1980. It tackled a basic coordination problem: how to assign tasks in a distributed system. The setup was simple but powerful:

• One agent (the “manager”) has a task it can’t handle alone.

• It broadcasts the task to other agents.

• Agents that can do the job submit bids.

• The manager picks the best one for the task.

This was a lightweight, market-style way of getting things done across a network. It allowed for flexible coordination – agents could come and go, tasks could shift hands, and everything was done through negotiation rather than strict control. While it may seem basic now, the Contract Net introduced important ideas that still shape MAS today: breaking down tasks, sharing the load, and letting agents self-organize.

These early ideas helped push the field toward a bigger realization: intelligence doesn’t have to be centralized. Sometimes it emerges from how independent agents work together.

Control Net Protocol introduced decentralized control and dynamic resource allocation, principles that remain foundational today. Recent research incorporates reinforcement learning to enable adaptive bidding and strategy refinement. The MAS paradigm’s emphasis on interaction-driven intelligence, grounded in game theory, has catalyzed innovations in blockchain systems and IoT coordination.

Core Components of MAS

To understand a Multi-Agent System, we need to dissect its fundamental building blocks. MAS are built on four pillars →