Multi-agent applications are systems where multiple AI agents work together, each handling specialized tasks, to solve complex problems that require different skill sets and perspectives.
Nemotron 3 Super excels in multi-agent scenarios because its large context window allows a single agent to maintain long conversation histories and coordinate with other agents without losing information. In software development, one agent might handle code generation, another code review, and a third handles testing coordination. In cybersecurity, agents can specialize in threat detection, incident response, and policy enforcement while sharing context through a central coordination layer.
With Milvus as your vector storage layer, you can build persistent knowledge stores that agents access and update. Each agent can retrieve relevant context from Milvus (threat intelligence, code patterns, past decisions) and contribute findings back to the shared vector store. This architecture enables sophisticated workflows where agents learn from each other without requiring all agents to run simultaneously. Agentic RAG with Milvus and LangGraph provides patterns for building these systems.