In recent months, we have not stopped hearing about artificial intelligence. What if agents, what if LLMs, what if RAGs... In a previous article we were talking about how to build generative agents that don't just respond, but execute. Today we go one step further: what happens when that agent needs to access private data, provide accurate responses or operate at scale?

In the midst of the hype, the important thing remains the same as always: understand what we want to solve and design an architecture that makes sense for that purpose.

In this post we tell you how we do it at Secture when we set up RAG systems with AI agents, what mistakes we have learned to avoid and what details make the difference when you actually take it to production.

Why RAG architecture?

Because not everything can be left in the hands of a generic model. RAG (Retrieval Augmented Generation) allows you to keep control over your data, avoid hallucinations and give useful answers using real knowledge of your business.

But before going into detail with the RAG architecture, I would like to make a differentiation between the different ways we have to use the technology when it comes to having an agent.

• Basic or standalone agentsno real connection with your data. They respond according to the model training, but have no context about your business. Good for experimentation, limited for production.
• Agents with connectors: allow you to make calls to APIs or external tools. They go a step further, but their logic is linear and limited to the predefined script or flow.
• Intelligent orchestrated agentsare modular systems that combine multiple processes (search, validation, formatting, quality control). They are integrated with internal databases and can be adapted to different use cases with precision. This is where RAG shines.

RAG architecture: the essentials

Everything starts from here:

• Private data layer, without exposing sensitive information to third parties.
• Vector database (yes, Postgres works).
• Thoughtful intakeIt is not enough to upload PDFs in a crazy way.
• Semantic indexingevery vector counts.
• Agents that do specific things: search, sorting, formatting, final check.

And yes, each one is a different process. It sounds like a mess, but when you orchestrate it right... it works wonders.

Keys that no one tells you (but you should be clear about)

• Don't expect magic. If you don't know what you want to solve, the AI won't guess.
• Think about the data from minute one. What do you have, where is it, how are you going to structure it?
• It's not all chatbots. You can generate quotes, do onboarding, filter candidates or set up brutal search engines.
• The intake phase is key. And the one that takes the longest. So take it seriously.

Does it scale?

It depends. If your project is for something small, with an LLM wrapper and four little things you'll be fine. But if you are handling sensitive data, many users or several use cases... better prepare a good architecture from the beginning.

We work with RAG for the data layer because it gives us freedom, security and flexibility. Does it do it all? No. Does it allow you to build very useful things that work today? Absolutely it does.

It also allows you to lay the foundation on which you can later grow. A huge plus.

Beyond the model, the real power is in the agent.

A language model, by itself, is powerful but limited. What really makes the difference is how you wrap it, what data you give it and what ability it has to act. AI agents are like living pieces of software that make decisions, process information and accomplish specific goals. They are not assistants, they are part of the team.

RAG is not the destination, it is the means. What we seek with these architectures is not only to respond accurately, but to empower agents to do things that are useful, of quality and aligned with your business. From searching for internal documents to generating business proposals or automating internal processes: if you orchestrate it right, your agent can go from being a curious toy to an indispensable tool.

If you want to find out how to create a RAG architecture step by step, see this workshop we did in our offices.