Multi-agent AI systems face unique challenges in failure recovery due to their stateful nature, learning capabilities, and requirement to maintain context over extended periods. Traditional failure recovery patterns designed for stateless microservices are insufficient, as they cannot account for the complex interdependencies between agents and their collective state. Effective failure recovery requires proactive strategies that anticipate and mitigate potential failure modes during the architecture phase. Designing communication protocols that degrade gracefully, prioritizing critical coordination messages, and using lightweight acknowledgment patterns can help prevent premature timeouts and false failure signals. Isolating failure domains, implementing circuit breakers with adaptive triggers, and creating isolation boundaries that preserve collaboration are also crucial. When restoring multi-agent systems after failures, careful planning is required to restore systems to a consistent state while avoiding secondary failures. Determining the right recovery approach depends on the nature of the failure and the system's operational requirements, and building decision frameworks that evaluate the scope of failure and system conditions in real-time can support this adaptability.