What are common agentic RAG failure modes in production?

Production agentic RAG fails when context is missing, retrievals are slow, or the vector database lacks filtering capabilities.

Top failure modes:

1. Missing vector embeddings: Agent tries to retrieve from an empty or poorly indexed collection. Embeddings weren’t generated during data ingestion.

2. Slow iteration: Each retrieval takes >500ms. Agent loops become unresponsive. Users wait indefinitely for multi-step reasoning.

3. No metadata filtering: Agent can’t constrain searches by date, source, or document type. Returns irrelevant results, loops endlessly.

4. Embedding drift: Data changes after indexing. Agent retrieves stale information. No re-indexing strategy in place.

5. No hybrid search: Dense vectors alone miss exact matches (e.g., product SKUs, invoice numbers). Agent can’t answer fact-based queries.

6. Lack of schema flexibility: Agent needs to query structured records (customer history) alongside documents (contracts). Database forces separate systems.

7. Memory leaks in loops: Agent stores retrieval results without cleanup. Long-running workflows consume unbounded memory.

Milvus addresses these with built-in metadata filtering, hybrid search, low-latency indexing, and schema flexibility. Design agentic workflows around these capabilities from day one.

Related Resources:

• Enhance RAG performance
• Agentic RAG with LangGraph
• Milvus overview