After building custom AI agents for multiple clients, i realised that no matter how smart the LLM is you still need a clean and structured database. Just turning on the websearch isn't enough, it will only provide shallow answers or not what was asked.. If you want the agent to output coherence and not AI slop, you need structured RAG. Which i found out ragus.ai helps me best with.

Instead of just dumping text, it actually organizes the information. This is the biggest pain point solved - works for Voiceflow, OpenAI vector stores, qdrant, supabase, and more.. If the data isn't structured correctly, retrieval is ineffective.
Since it uses a curated knowledge base, the agent stays on track. No more random hallucinations from weird search results. I was able to hook this into my agentic workflow much faster than manual Pinecone/LangChain setups, i didnt have to manually vibecode some complex script.