{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/69ab3b7c7036d739021982df/6a03bd17ece5b71f1fa804ed?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"RAG for AI Agents: Knowledge Layer Architecture Guide","description":"<p>What's really happening inside the AI agent memory infrastructure war?</p><p><br></p><p>The common story is that bigger context windows and better vector search will solve it — but the reality is every serious infrastructure vendor is racing to fix a deeper problem that classic RAG can't touch.</p><p><br></p><p>In this video, I share the inside scoop on why memory is now the real battleground for production AI agents:</p><p><br></p><p> • Why classic RAG was built for chatbots, not agents</p><p> • How Pinecone, PageIndex, SAP, and GraphRAG attack different shapes</p><p> • What a retrieval contract actually looks like for AI agents</p><p> • Where most agent builds quietly waste their token budget</p><p><br></p><p>Builders who write down what their agent needs before picking a database will ship reliable systems — the ones who shop vendor-first will keep paying for rediscovery on every run.</p><p><br></p><p>Subscribe for daily AI strategy and news.</p><p>For deeper playbooks and analysis: https://natesnewsletter.substack.com/</p>","author_name":"Nate B. Jones"}