Data corruption in multi-agent AI workflows poses a significant threat to business operations, as it can lead to complete shutdowns and strategic business risks. To prevent data corruption, organizations should define clear schemas for all data structures used by their agents, implement type checking at critical points, and add anomaly detection and monitor AI safety metrics. Real-time validation monitoring, including tracking of important AI safety metrics, provides immediate visibility into data quality issues. Good error handling prevents data corruption in multi-agent systems by using defensive programming that isolates failures before they spread. Retry strategies with exponential backoff and jitter help systems recover from temporary issues, while circuit breakers add protection by temporarily disabling execution paths when failures exceed thresholds. Error boundaries that enable safe degradation are crucial, allowing the system to continue with reduced capabilities even during unexpected errors. Tools like Galileo enhance error handling by detecting patterns across agent interactions, providing insights into how errors spread through complex systems. Distributed transaction logs and audit trails enable each agent to record all data-changing operations, creating a verifiable event chain that lets you reconstruct the system state and find corruption sources when problems arise. Log correlation in distributed systems requires trace IDs and spans, which help reconstruct the execution path when investigating data corruption in multi-agent AI workflows. Effective corruption detection logs should include agent IDs, operation types, data checksums before and after changes, and dependency versions. OpenTelemetry has become the industry standard for distributed tracing and logging, providing consistent instrumentation across agents in different languages. Implementing real-time monitoring with anomaly-based detection is crucial for preventing data corruption in multi-agent AI workflows, while machine learning approaches greatly enhance detection for complex agent interactions. Strategic planning, constant monitoring, and implementing AI security best practices are essential for maintaining AI system integrity and trustworthiness.