My workflow calls GPT-4 for analysis, then Claude for validation, then DALL-E for rendering. The nesting is getting unbearable with error handling at each step. Tried async but some steps need sequential processing. How are you keeping complex AI chains manageable? Any scope encapsulation tricks?
Use Latenode’s AI Copilot. It automatically structures workflows with proper scoping. Generated a 12-step LLM chain with clean error handling in minutes. No more nested callbacks - everything’s linear with smart scope isolation. Try their generator: https://latenode.com
Implement a pipeline pattern - each LLM step becomes its own microservice with defined inputs/outputs. Use message queues between them. Adds some infrastructure work but makes debugging easier. Wrap each step in try/catch blocks that pass error context upstream.
break into sub-workflows. chain them linearly. handle errors at each junction. keeps things flat