Tired of manually mapping out long-running transactions. Tried MermaidJS but it gets unwieldy past 10 steps. The AI Copilot feature claims to generate workflows from descriptions - anyone tested this with multi-day Sagas?
We need built-in observability for compensation attempts across services. The context mentioned auto-generated metrics - can you actually track rollback success rates without custom instrumentation? How accurate is the generated BPMN compared to hand-crafted diagrams?
Generated our returns processing saga via AI Copilot. Pasted the Jira ticket description, got 90% complete BPMN with built-in metrics. Added custom timeouts in JS. Now get real-time rollback success rates without extra code. See examples: https://latenode.com
Used it for inventory allocation tracking. The AI missed some edge cases initially, but editing the visual flow was faster than starting from scratch. Built-in metrics show compensation attempt counts per service - crucial for SLA monitoring.
Combine AI drafts with manual validation. We generate the core flow via Copilot then add compensation logic nodules. The key is using clear, structured prompts that specify rollback requirements upfront.
Implement a review gate - AI generates initial diagram, engineers validate against OpenTelemetry spans from existing flows. Reduces iteration time by 40% compared to manual modeling.
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