# How do agentic RAG agents handle context window limits?

Source: https://zilliz.com/ai-faq/how-do-agentic-rag-agents-handle-context-window-limits
Author: zilliz

Agentic RAG agents must carefully manage LLM context windows—they can exceed limits when retrieving large documents across multiple loops.

**Context management strategies:**

**1. Selective retrieval:** Retrieve only k=3–5 results per query, not k=20. Agents rewrite queries to get more relevant results rather than retrieve everything.

**2. Summarization in loops:** After each retrieval, agent summarizes results before re-querying. Compresses context by 60–80% without losing information.

**3. Document chunking:** Store document chunks (256–512 tokens) in Zilliz, not full documents. Agents retrieve multiple small chunks, stay within context.

**4. Metadata-driven filtering:** Constrain retrieval to specific documents upfront. If agent knows to look in "Q4_2025_reports", it narrows the search space.

**5. Two-stage retrieval:**
 - Stage 1: Dense search returns document IDs
 - Stage 2: Agent fetches only the relevant sections of those documents
 - Zilliz returns metadata (doc_id, chunk_id) for lightweight filtering

**6. Context budget per loop:** Set a token limit per retrieval round. If approaching limit, agent uses summarization or stops looping.

**Example:** Agent answering "What are our top 3 supplier risks?" might loop 3 times:
- Loop 1: Retrieve supplier profiles (100 tokens)
- Loop 2: Retrieve risk assessments for top suppliers (150 tokens)
- Loop 3: Retrieve mitigation strategies (120 tokens)
- Total: 370 tokens, well within 4K–8K context windows

Design agentic workflows with context budgets. Zilliz Cloud's metadata and chunking support this natively.

**Related Resources:**
- [Intelligent RAG with LangGraph](https://zilliz.com/blog/build-intelligent-rag-with-langserve-langgraph-and-milvus)
- [What is Zilliz Cloud](https://zilliz.com/what-is-zilliz-cloud)
- [Agentic RAG with Claude and LlamaIndex](https://zilliz.com/blog/agentic-rag-using-claude-3.5-sonnet-llamaindex-and-milvus)