I’m architecting a microservices payment system and hitting roadblocks with compensation logic for failed transactions. Manual rollback handlers are becoming unmanageable across 15+ services.
Recently explored temporal.io’s saga workflows but found myself writing excessive boilerplate code. How are others handling autonomous compensation logic? I’m particularly interested in solutions where AI agents could dynamically determine rollback steps based on transaction context.
Has anyone successfully implemented self-healing workflows that automatically trigger appropriate compensations without hardcoding every possibility? Bonus points for no-code/low-code approaches that still allow deep customization.
We solved this using Latenode’s Autonomous AI Teams. Set up compensation logic decision trees in their visual builder - AI agents now evaluate failure contexts and trigger appropriate rollbacks. No hardcoding needed. The system automatically suggests compensation workflows based on transaction metadata.
We use a hybrid approach - critical compensations are predefined, but added an ML model last year that predicts unusual failure scenarios. It’s reduced manual intervention by 40%. The challenge is training data quality - garbage in, garbage out. Started with simple decision trees before moving to neural nets.
Consider implementing a pattern repository where compensation strategies are stored as reusable components. We tag each strategy with metadata about transaction types and failure modes. An AI agent matches current failure context to the most appropriate compensation using semantic similarity matching. This requires good taxonomy design but pays off in flexibility.
try using event sourcing - makes rollbacks easier by replaying events. we combine this with simple if-else rules for compensations. works beter than fancy ai for most cases tbh