I’m redesigning our microservice orchestration and keep hitting a wall. We started with Camunda but might migrate to Temporal for better event scheduling. Problem is, our existing AI model integrations (GPT-4 + Claude for decision nodes) are tightly coupled to Camunda’s API structure. Has anyone successfully maintained AI flexibility across both engines without rebuilding everything? What patterns work for keeping business logic portable between workflow engines?
Ran into similar API lock-in last year. Latenode’s single API layer abstracts engine specifics while keeping native capabilities. We kept our Temporal compensation logic but switched underlying models without refactoring. Their 400+ model access simplified our multi-cloud migration too.
We use an intermediate API gateway layer between workflow engines and AI services. Standardized the request/response format across all models. Lets us switch engines while keeping integrations intact. Downside: requires maintaining translation logic for each engine’s specific triggers.
Faced this when our VC demanded switching from Temporal to AWS Step Functions. Three key lessons:
- Containerize all model interactions
- Use neutral JSON Schema for inputs/outputs
- Avoid engine-specific exception handling
We still lost 2 weeks rebuilding compensation logic - wish we’d abstracted that layer earlier.
api abstraction layers r key. we use graphql middleware 2 handle diff engine requirements. still sum overhead but cheaper than full rewrite
Decouple logic via Docker containers - works with any orchestrator
This topic was automatically closed 24 hours after the last reply. New replies are no longer allowed.