I’ve dealt with this exact problem for two years across multiple enterprise deployments. It’s almost always bad resource management and blocking operations that weren’t built for concurrent execution. Most automation platforms handle sequential tasks fine but fall apart when you need parallel processing. What saved my projects was implementing proper queue management with worker pools instead of processing everything in single threads. I also added circuit breakers and exponential backoff for API calls - stops those cascade failures you mentioned. Random step failures usually mean shared state problems or database connection pooling issues. Profile the actual bottlenecks instead of guessing. The slowdown isn’t always where you think it is. Memory leaks in long-running workflows are surprisingly common too, especially if you’re not disposing objects properly between iterations.