Robust behavior in complex, dynamic environments mandates that intelligent agents autonomously monitor their own run-time behavior, detect and diagnose failures, and attempt recovery. This challenge is intensified in multiagent settings, where the coordinated and competitive behaviors of other agents affect an agent’s own performance. Previous approaches to this problem have often focused on single agent domains and have failed to address or exploit key facets of multi-agent domains, such as handling team failures. We present SAM, a complementary approach to monitoring and diagnosis for multi-agent domains that is particularly well-suited for collaborative settings. SAM includes the following key novel concepts: First, SAM’s failure detection technique, inspired by social psychology, utilizes other agents as information sources and detects failures both in an agent and in its teammates. Second, SAM performs social diagnosis, reasoning about the failures in its team using an explicit model of teamwork (previously, teamwork models have been employed only in prescribing agent behaviors in teamwork). Third, SAM employs model sharing to alleviate the inherent inefficiencies associated with representing multiple agent models. We have implemented SAM in a complex, realistic multi-agent domain, and provide detailed empirical results assessing its benefits.