r/SpringAIDev

At this presentation at Spring I/O conference, Thomas Vitale presents different RAG architectures that you can build using SpringAI.

What I liked at this presentation is that it presents different approaches of data flows, pipelines architectures, storing and indexing mechanisms that you can apply to build your RAG pipeline. And all of them supported by SpringAI.

Enjoy!

So I deployed an AI agent for a client a few months ago.

It worked. Like technically it worked fine. But every time someone asked it something company specific, past decisions, internal policies, how they'd handled a situation before, it just had nothing. It would hallucinate or give a generic answer or ask for context that should've already been there.

The fix everyone reaches for is stuffing everything into the system prompt. Which works until it doesn't. You hit context limits, it gets stale, and you're manually maintaining a document that nobody trusts.

I'm a CS freshman and I've been building something on the side for about 3 weeks called Lore. Institutional memory as an API. You point it at your Slack or Notion or docs, it extracts decisions your team has made, builds judgment rules from patterns, and your agents can query it at runtime before they respond.

So instead of the agent being a smart day-one hire, it actually starts with company context.

The architecture is the part I'm most interested in getting feedback on. A few things under the hood:

• R3Mem style multi-level memory, episodic events roll up into semantic patterns which roll up into rules. Inspired by the paper.
• GAAMA style concept nodes with dynamic taxonomy so the graph isn't just static categories, it evolves as the company's language evolves
• Bi-temporal modeling so you always know what the company believed at a given point in time, not just what's true now. Policy changed in February? The agent knows not to apply the old rule to new queries.
• Causal event nodes so decisions aren't just stored, they're linked to what caused them and what they caused downstream
• Semantic deduplication so you don't end up with 40 slightly different versions of the same decision
• Confidence scoring on every extracted decision so agents know how much to trust what they're retrieving

Still pre-launch. Haven't had a real user touch it yet. Before I go find one I wanted to ask people who've actually built agents in production:

1. Is this a real pain or do you solve it some other way?
2. What data source would matter most to you, Slack, Notion, email, something else?
3. What would it take for you to actually trust the extracted rules enough to let an agent act on them?

Honest answers only. Happy to go deep on any part of the architecture if anyone's curious.

https://preview.redd.it/wo8hzusyiqzg1.png?width=1669&format=png&auto=webp&s=1948d300666e5f38881e88bc4b31d7122cb613b2

In this talk, Yuri Bezsonov present how to leverage SpringAI to build agentic AI systems. In his presentation, he shows:

• How agentic AI solutions evolved from simple assistants to full autonomous agents
• How the agent loop enables an agent to perform complex, multi-step and reasoning actions

After introducing these fundamentals, then he proceeds to a practical application of agentic AI implementation with RAG and MCP tools integration.