# How do agentic RAG agents handle irrelevant retrieval results?

Source: https://zilliz.com/ai-faq/how-do-agentic-rag-agents-handle-irrelevant-retrieval-results
Author: zilliz

Agentic RAG agents evaluate retrieved documents and iteratively re-query or rewrite prompts if results are irrelevant.

Agent strategies:

**1. Relevance scoring:** Agent uses an LLM to score whether retrieved documents answer the original query. If scores fall below a threshold, the agent re-queries with different search terms.

**2. Query rewriting:** If retrieval fails, the agent reframes the question. Example:
- Original: "What is our Q4 supply chain resilience?"
- Rewritten: "Which suppliers had >10 days delivery delays in October–December?"

**3. Multi-step iteration:** Agent chains retrievals. If the first retrieval finds related documents, the agent uses those results to inform a second, more targeted query.

**4. Fallback strategies:** If vector search returns sparse results, the agent switches to metadata filtering, keyword search, or broader semantic queries.

**5. Metadata constraints:** Agent applies dynamic metadata filters (date ranges, document types, source systems) to prune irrelevant results before generation.

This iterative loop is why low-latency retrieval matters—agents need multiple round-trips to Zilliz Cloud without blocking user response times. Zilliz Cloud guarantees <100ms p95 latency even under agent query spikes.

**Related Resources:**
- [Intelligent RAG with LangGraph](https://zilliz.com/blog/build-intelligent-rag-with-langserve-langgraph-and-milvus)
- [Local agentic RAG with LangGraph](https://zilliz.com/blog/local-agentic-rag-with-langraph-and-llama3)
- [Agentic RAG with Claude and LlamaIndex](https://zilliz.com/blog/agentic-rag-using-claude-3.5-sonnet-llamaindex-and-milvus)