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Multi-agent systems are computational systems composed of multiple interacting intelligent agents—autonomous software entities that perceive their environment through sensors and act upon it through actuators—operating within a shared environment to collectively solve problems that are beyond the capacity of any single agent. Each agent is characterized by autonomy, social ability, reactivity to environmental change, and proactivity toward goals, and agents may be homogeneous or heterogeneous in their capabilities, knowledge, and objectives ^[1]. The system-level behavior emerges from local interactions governed by communication protocols, negotiation strategies, and coordination mechanisms, rather than from centralized control. Key structural dimensions include the degree of agent autonomy, the topology of the interaction network, the distribution of information, and the conflict or cooperation relationships among agent goals. Agents may cooperate to achieve joint objectives, compete through auction or bidding mechanisms, or negotiate through argumentation protocols, and system properties such as global coherence, stability, and scalability emerge from these local interaction rules ^[2]. The theoretical foundation distinguishes multi-agent systems from distributed systems in that agents are intentionally autonomous and goal-directed, rather than merely executing distributed functions under central orchestration, and from expert systems in that problem-solving is inherently social and interactive ^[3].