Debugging multi-agent systems involving collaborating large language models (LLMs) presents unique challenges due to their decentralized and partially observable nature, which transforms minor issues into complex detective work. Traditional debugging techniques often fail as these systems struggle with non-deterministic outputs, hidden agent states, memory drift, and cascading errors. Debugging becomes even more complicated with tool invocation failures and emergent behaviors arising from unexpected interactions between agents. The absence of reliable evaluation metrics and resource contention exacerbates these difficulties, leading to significant bottlenecks and system unreliability. To mitigate these challenges, teams are encouraged to implement strategies such as deterministic test modes, comprehensive logging, and intelligent resource management. Tools like Galileo offer real-time monitoring and robust debugging frameworks that enhance system reliability and observability by providing solutions such as evaluator guardrails, JSON schemas, and adaptive pooling, ultimately transforming debugging from a reactive to a proactive process.