Supply chain optimization, customer support, legal research, and code generation are the dominant agentic RAG use cases in 2026.
1. Supply chain agents:
- Retrieve supplier profiles, shipment history, risk assessments
- Rewrite queries: “Which suppliers had delays?” → “Which suppliers had >10-day delays in Q4 2025?”
- Loop: Find suppliers → Check their risk scores → Evaluate alternatives
- Milvus stores supplier embeddings, delivery history, contract data
2. Customer support agents:
- Retrieve interaction history, ticket history, product info
- Handle multi-turn reasoning: “What subscriptions does this customer have? Are they eligible for discounts?”
- Loop until customer query is resolved
- Milvus stores customer profiles, past interactions, product embeddings
3. Legal research agents:
- Retrieve relevant case law, regulations, contract templates
- Query rewriting: “Is this clause enforceable?” → “Find precedents where similar clauses were challenged”
- Loop: Find cases → Extract precedents → Synthesize ruling
- Milvus stores case law embeddings, regulatory documents, contract precedents
4. Code generation agents:
- Retrieve code snippets, API docs, stack overflow examples
- Rewrite: “How do I sort a list?” → “How do I sort a list in language X with constraint Y?”
- Loop: Find similar code → Adapt template → Generate implementation
- Milvus stores code embeddings, API documentation, code examples
All share a pattern: agents retrieve context dynamically, iterate when initial results are irrelevant, and synthesize answers across multiple sources. Milvus is the memory layer for all of these.
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