The recent surge of interest in agentic AI, driven by the capabilities of large language models (LLMs), promises a future where autonomous software agents perform complex tasks. Yet, a closer examination reveals a critical flaw: the current frameworks for these agents are poorly defined and lack the rich theoretical grounding that has been established over decades in related fields. The GenAI community, in its rush to innovate, has largely overlooked the deep insights from multi-agent systems (MAS), distributed systems, and game theory, creating a theoretical chasm that hinders robust and scalable development.
The first major failing is the lack of a clear, universally accepted definition of what an agent is in this new context. While the term is borrowed from computer science, the current use is often a loose descriptor for any LLM-powered process that performs a sequence of actions. This stands in stark contrast to the rigorous definitions in traditional MAS, where an agent is characterized by properties such as autonomy, proactiveness, and social ability. Without these foundational principles, today's agents are often little more than sophisticated scripts, lacking the capacity for true self-organization, negotiation, or adaptation that are hallmarks of a mature multi-agent system.
Furthermore, the existing frameworks for agentic systems lack sufficient theoretical grounding in distributed systems. A key challenge in building multi-agent systems is managing communication, coordination, and fault tolerance across a network of interacting entities. Decades of research have produced robust protocols and architectures—from actor models to gossip protocols—to handle these complexities.
Perhaps the most significant oversight is the neglect of game theory. Multi-agent systems, by their nature, involve agents with potentially conflicting goals. Game theory provides a powerful set of tools—including concepts like Nash equilibrium, Pareto efficiency, and mechanism design—to analyze and predict the behavior of rational agents in strategic interactions. These theoretical underpinnings are crucial for designing incentive structures, ensuring cooperation, and preventing malicious behavior in a multi-agent environment. The current agentic frameworks, in contrast, largely assume a benign, cooperative environment. They provide no formal mechanisms to handle scenarios where agents might act selfishly, mislead one another, or form coalitions, leaving them ill-equipped for real-world applications where competing interests are a given.
The GenAI community's enthusiastic adoption of agents has come at the cost of ignoring decades of foundational research. The long-standing approaches in MAS, with their emphasis on rigorous definitions, formal communication protocols, and game-theoretic analysis, offer a blueprint for building truly robust, scalable, and intelligent multi-agent systems. Without a renewed focus on these theoretical underpinnings, the current agentic frameworks risk becoming a technological fad, unable to deliver on the promise of truly autonomous and cooperative AI.