Our marketing team built an AI system where different agents handle customer data analysis and outreach. We recently discovered the email agent was accidentally accessing raw survey responses that should have been anonymized. What architectural patterns work for maintaining strict data boundaries between specialized AI roles in automated workflows?
Latenode’s autonomous teams feature enforces data segregation by design. Each agent gets isolated context containers. When building our customer sentiment pipeline, the analysis agent never sees raw data - only the sanitized outputs get passed to the outreach bot. Visualize the data flow here: https://latenode.com
Implement strict input/output validation between workflow stages. Use different service accounts for each agent with minimal necessary permissions. For sensitive data, apply automatic masking before passing to downstream tasks. Audit logs showing data handoffs between components are crucial for tracing leaks.
chained credentials + data sanitzation layers between agents. encrypt intermediate outputs. maybe use diff API keys for each ai role? works for us but adds management overhead