RAG joins the agentic stack — make it enterprise-safe with private AI  

Recently you may have seen online commentary suggesting that enterprises are shifting away from traditional retrieval augmented generation (RAG) architectures due to risks associated with centralizing vector stores, broken access controls and latency.

But RAG is not dead. Rather, it’s continuing to be leveraged in agentic AI stacks. We are entering an era where RAG will combine with one agent doing this, another agent doing that, plus some multimodal interfaces and so on. It's part of the future of building enterprise-grade AI agents (rather than just models) under full governance, security and control.

Old RAG is now new RAG

RAG retrieves relevant documents from a knowledge base and feeds them into the LLM along with an “improved” user query, so answers are grounded in enterprise content rather than the model’s memory.

That function hasn’t gone away; it has become a key ingredient in a signature dish in which multiple agents coordinate tasks, tools and data sources.

RAG in the agentic AI mix supports the autonomous actions of these systems, providing verifiable, current context for decision making; enabling domain- and task-specific intelligence for adaptability; reducing hallucinations and ensuring traceability for security and governance; avoiding retraining for efficiency; and acting with situational awareness for autonomy.  

More benefits, less risk

This means you can leverage GenAI’s decision-making capacity without risking data breaches or unauthorized access. You gain unrestricted data volume and processing capabilities to support large data loads, real-time data processing and large-scale simulations, as well as AI model customization. In addition, you can change or fine-tune models to meet specific business challenges.

Working with a partner significantly reduces costs. This is particularly true for expenses involving inference, fine-tuning, deployment and maintenance, and for acquiring the expertise to comply with private AI infrastructure security regulations.

Map the work, model the agents

How to best leverage this maturing framework? Before you stand up agents, document the workflow the traditional way. Determine the steps, decisions, actions and data at each point.

• Divide the process into clear stages, then assign agent roles to each stage. For example, in the case of an insurance agentic AI process, an intake agent extracts fields, a risk agent fetches credit signals, a pricing agent proposes terms and a reviewer agent checks compliance.
• Decide which new agents you need, the decisions they should make and the data they’ll need. When data lives behind enterprise systems, expose it safely. Many teams implement model context protocol (MCP), an open protocol that enables models to communicate with data and tools (e.g., agents) through standard servers.  

Once the flow and agents are sketched, write evaluations and tests to prove the system meets expectations end-to-end. Organizations have already demonstrated how to do this at scale: Morgan Stanley evaluated GPT-4 against expert ground truth and restricted responses to internal content — a pattern that translates easily to agent validation and auditability.