Multi-Agent systems

Multi-Agent systems are collections of autonomous software or robotic agents that perceive their environment, reason, and coordinate to achieve individual or shared goals. Each agent follows its own policy—rule-based, machine-learning, or large-language-model driven—while communicating through messages, shared memory, or a blackboard. System-level behaviors emerge from local interactions: task allocation, negotiation, cooperation, and sometimes competition. Key architectures include centralized coordinator, fully distributed peer-to-peer, and hybrid market-based models. Metrics such as scalability, robustness, and convergence rate gauge performance, and protocols like contract nets or consensus algorithms ensure conflict resolution. Applications span swarm drones, smart grids, supply-chain optimization, and AI copilots where specialized agents—planner, solver, critic—collaborate to answer complex queries. Challenges involve communication overhead, security, and aligning agent incentives, mitigated by reward shaping, cryptographic channels, and simulation-based testing.