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:
Missing vector embeddings: Agent tries to retrieve from an empty or poorly indexed collection. Embeddings weren't generated during data ingestion.
Slow iteration: Each retrieval takes >500ms. Agent loops become unresponsive. Users wait indefinitely for multi-step reasoning.
No metadata filtering: Agent can't constrain searches by date, source, or document type. Returns irrelevant results, loops endlessly.
Embedding drift: Data changes after indexing. Agent retrieves stale information. No re-indexing strategy in place.
No hybrid search: Dense vectors alone miss exact matches (e.g., product SKUs, invoice numbers). Agent can't answer fact-based queries.
Lack of schema flexibility: Agent needs to query structured records (customer history) alongside documents (contracts). Database forces separate systems.
Memory leaks in loops: Agent stores retrieval results without cleanup. Long-running workflows consume unbounded memory.
Zilliz Cloud eliminates infrastructure failures: managed autoscaling handles retrieval latency, hybrid search is built-in, and schema flexibility is standard. Enterprise teams can focus on agent reasoning without database reliability headaches.
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