Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
Data Science
Towards AI·Jun 15·5 min read
5 Failure Modes in Production Agentic RAG That No Architecture Diagram Will Show You
TL;DR
After deploying an Agentic RAG (Retrieval-Augmented Generation) system, unexpected issues arose, including latency cliffs and memory drift. The system was built using LangGraph, featuring five layers and three types of memory.
✦ Why It Matters
Engineers should implement robust monitoring and evaluation strategies to identify and address hidden issues in deployed systems.
Key Takeaways
- Monitor system performance continuously after deployment.
- Implement strategies to address latency spikes proactively.
- Regularly evaluate memory usage to prevent drift.
- Be aware of potential prompt injection risks in production.
Full Summary
Agentic RAG systems, which combine retrieval and generation capabilities, often face hidden challenges that emerge only after deployment. In this case, a system was constructed using LangGraph, which organizes components into five layers and incorporates three distinct types of memory.
Key issues identified included latency cliffs, where response times unexpectedly spike, and memory drift, where the system's performance degrades over time. Additionally, reflection loops and prompt injection patterns posed risks to system integrity.
Evaluation work, which involves assessing system performance and user interactions, accumulated unnoticed, complicating maintenance. These findings emphasize the importance of continuous monitoring and adaptation in production environments, as initial designs may not reveal all potential failure modes.