Multi-agent context sharing enables autonomous agents to exchange situational data, goals, and learned experiences to achieve coordinated behavior. Approaches include shared blackboard systems, direct message passing, and distributed knowledge bases, each balancing latency, scalability, and consistency. Key challenges involve maintaining up-to-date context across dynamic networks, handling conflicting information, and ensuring privacy and security. Techniques such as context versioning, relevance filtering, and trust models help mitigate these issues. Applications range from collaborative robotics and smart grids to distributed decision support, where agents adapt to evolving environments by leveraging shared insights. Effective design requires clear protocols, robust synchronization, and mechanisms for conflict resolution, ensuring that shared context enhances overall system performance without overwhelming communication channels.