Introducing our new agentic RAG framework. A collab with Google Cloud, our multi-agent workflow goes beyond standard RAG by breaking down complex enterprise queries & iteratively searching for sufficient context before generating dependable responses. 📜→

I think the Sufficient Context Agent's approach of pinpointing exactly what's missing and triggering targeted re-searches is the key breakthrough here. This directly addresses the biggest pain point in enterprise RAG where information is scattered across multiple databases, and I can see it being immediately useful for production customer support and internal knowledge systems. #AgenticRAG #Gemini

the shift from "retrieve once, stuff the context" to "iteratively search until sufficient" is the whole ballgame. standard RAG fails because it guesses how much it needs up front. letting the agent decide when it has enough is what makes it feel like it reasoned.

I believe, RAG is not the biggest problem when it comes to enterprises. It is usually RBAC

Agentic RAG is a game-changer for enterprise apps. Breaking down complex queries into iterative search steps solves the 'hallucination' bottleneck we often see in standard RAG. At Hunter Tech, we’re excited to see this scale! #GoogleCloud #AI #RAG #DevOps

Progress should not be slowed down. But acceleration without observability is fragile. Core ASA is about seeing trajectory before drift compounds.

That's exactly what I needed for my #BuildWithGemini submission 🙏

the honest answer is that most ai projects still rely on manual tuning and tweaking to get decent results. our agentic rag framework tries to change that by automating the process, so you don't have to be an expert in 20 different domains.

Dont stop AI research PLZ

It is fine they finally implemented one of the tutorials in langgraph website. Cute graph. Tho.

Can you provide an indexed cost comparison of the Vanilla RAG vs. Cross-Corpus Agentic RAG vs. Single-Corpus Agentic RAG? Would be helpful to know.

Persistence is All you Need.

this iterative context gathering is exactly what's needed. curious how you're handling the latency trade-off when the agents have to perform Multiple search hops before generating the final responsee

Interesting direction. An agentic RAG framework that iterates until it has enough context is closer to how messy enterprise questions actually behave, but the hard part will be showing where it beats standard RAG on latency, cost, and answer quality.

Security issues cannot be solved by external control layers. Agentic RAG for enterprise platforms is exactly that. It results in a massive increase in thermodynamic cost. The system wastes incredible amounts of free energy and computing power. Ask Gemini

nice. first thing i'd look at is how the agents hand off work and report state to each other. running a few in parallel daily, that's where things quietly fall apart way before retrieval quality does

Gotta understand it

Interesting Any benchmark results?

yeah +1. “fixing” is the easy part to demo. the real fight is detection + a regression signal you trust. otherwise it’s just auto-generating new failure modes faster than you can read traces.

Breaking complex queries into iterative context search is a useful distinction from single-pass RAG. The key is measuring when the system has enough evidence to answer.

Agentic RAG should be evaluated on retrieval sufficiency, not just answer quality. The best product surface shows the search decisions: what was tried, why the context is enough, and when the agent should stop.

@readwise save

What is the latency of this approach?

Can this be used with external retrieval tools?

super cool collab, love the multi-agent angle. curious how it handles conflicting context from diff sources... does it surface that or just try to resolve behind the scenes?

👀👀👀

@Presidentlin

This is the solution I always needed !!

Keep your agents secure!

Lame. No code. Closed source

The sufficient-context check is the part I like most. It turns RAG from “search and hope” into something much closer to a real workflow.

Cost? Latency?

Love this direction—iteratively searching for sufficient context is exactly what standard RAG misses in enterprise setups. Multi-agent workflows + Google Cloud sounds like a strong combo for dependable answers. #RAG

@GoogleResearch, how does the workflow handle conflicting information across different enterprise databases during the iterative search process? Check out our post here:

To get AI results works great! The difference is that now people search for problems and AI suggest products as solutions, not legacy search.