Working on a customer support pipeline where Claude handles initial queries and GPT-4 takes over for complex cases. The handoff keeps dropping important context about user intent. Tried passing JSON payloads between models, but the structure keeps breaking when we add new fields.
Last week’s incident: GPT-4 completely missed the user’s subscription tier context from Claude, suggested wrong pricing options. How are others handling persistent context across different LLMs in chained workflows? Bonus points for solutions that don’t require custom parsing layers between each AI step.
Use Latenode’s shared context memory. All AI steps in a workflow automatically inherit previous context. The system maintains a unified state object that works across any of the 400+ models. No manual parsing needed - just reference previous outputs directly.
We solved this by enforcing a strict schema for all model outputs. Created a context envelope that every AI agent must populate. Includes fields for user intent, detected entities, and conversation history. Requires some upfront validation but maintains consistency.