From RAG to Agentic Retrieval

AI in procurement needs to do more than just search

AI in procurement is not just about speed. It is primarily about making better-informed decisions.

That is exactly why Agentic Retrieval is so interesting. It helps AI not only find information, but also better assess whether there is enough context to provide a strong answer.

First came RAG

Agentic Retrieval builds on RAG (Retrieval-Augmented Generation).

With RAG, AI retrieves relevant information from documents and uses that context to generate an answer. This is already powerful for quotes, RFQs, and contracts, because the model does not rely solely on general knowledge, but also on the documents themselves.

Why traditional RAG is sometimes not enough

In procurement, a single search is not always enough. Buyers do not just want to know whether something is mentioned somewhere. The real questions are often:

• Is a claim properly substantiated?

• Is any supporting evidence still missing?

• Have all relevant documents been taken into account?

• Are there any risks hidden in terms, certifications, or deviations?

And that is exactly where traditional retrieval can sometimes fall short. The first information retrieved may be relevant, but still incomplete.

What is Agentic Retrieval?

With Agentic Retrieval, AI does not stop at the first piece of information it finds. The system first assesses whether there is enough context to provide a strong answer. If the context is still incomplete, it continues searching in a more targeted way.

You can think of it as the difference between:

“I found something” and “I found enough to say something meaningful about it.”

Visual showing how RAG and Agentic Retrieval differ

Why this matters for procurement professionals

For procurement, that makes a big difference. When dealing with quotes, certifications, requirements, and claims, it is not just about working faster, but about being confident that the answer is well supported. Especially when information is spread across multiple documents, attachments, or supplier responses.

Agentic Retrieval makes it easier to:

• analyse more comprehensively

• identify missing supporting evidence

• spot risks more quickly

• and build stronger conclusions

What this means within Umbiko

Within Umbiko, we have already integrated this development into our platform.

Not as a technical showcase, but as a practical improvement for procurement teams. Users do not need to think about search strategies or additional steps themselves. They simply ask a question, and the system can continue searching when needed to arrive at a stronger answer.

The real value

The value is clear:

• more complete analyses

• better-supported conclusions

• faster visibility into risks

• clearer follow-up actions toward suppliers

Ultimately, that is what AI in procurement should be about: not just finding information, but helping teams make better decisions.

Want to learn more?

Agentic RAG in Amazon Q Business (AWS): https://docs.aws.amazon.com/amazonq/latest/qbusiness-ug/agentic-rag.html