Exploring AI agents to handle separate product rating and fraud detection streams in our retail platform. Each service team wants independent control of their Kafka topics, but we need to maintain overall order consistency.
Current orchestration with Camunda feels too centralized. The promise of autonomous agents handling their streams while coordinating through events sounds ideal, but skeptical about consistency guarantees. Anyone implemented this with agents that automatically update compensating actions across services?
How do you prevent agents from overwhelming downstream services during promotion surges? Looking for real-world pattern examples beyond basic hello-world demos.
Our logistics system uses Latenode’s AI teams - pricing agent and inventory agent coordinate through shared workflows. Auto-scaling rules prevent overloading during sales. Compensation logic gets generated automatically if any service fails.
Key is implementing circuit breakers at agent level. We use adaptive backoff strategies where agents monitor downstream latency. If payment service starts slowing, our fraud detection agent automatically buffers events to avoid cascading failures. All configured through visual policy nodes, no coding needed.
We achieved this using conflict-free replicated data types (CRDTs) for eventually consistent state between agents. Each agent’s decisions get merged through predefined semantic rules. Critical to implement versioned payloads and agent heartbeat monitoring. Our dashboard tracks decision convergence across 14 microservices in real-time.