AI 2026-11-03 3 videos

Infrastructure Makes AI Agents More Reliable

Reliable AI agents need infrastructure that controls data, compute, and workflow—not just a bigger model.

Summary

Reliable agents depend on infrastructure that makes information accessible, execution controlled, and decisions traceable. Mixedbread targets retrieval losses with multimodal search, while Factory keeps compute, identity, and inference inside the customer’s environment. Neo4j adds explicit execution, context, and procedural graphs so agents follow governed paths that work across runtimes. The approaches differ in focus, but each replaces improvised agent behavior with architecture: a hedge fund reached 98% accuracy, Open Compute supports customer-owned machines, and compiled graphs cut a loan-approval workflow from about 7.5 minutes to roughly 0.5–2 minutes.

The videos

Better Search Took This AI Agent From 36% to 98% Accuracy - Aamir Shakir, Mixedbread ↗ AI Engineer

Mixedbread replaces lossy OCR, chunking, embedding, and vector-search pipelines with multimodal retrieval, helping a hedge fund with over 1 billion documents raise answer accuracy from 36% to 98%.

Your AI Agents Shouldn't Care Where They Run - Nikhil Harithas, Factory ↗ AI Engineer

Factory’s Open Compute lets agents run on customer-owned machines with outbound-only connections and bring-your-own inference, keeping compute and LLM calls inside the customer’s VPC.

Make Your AI Agents Predictable With Graph Engineering - Zachary Blumenfeld, Neo4j ↗ AI Engineer

Neo4j’s graph engineering makes agent execution, context, and procedures explicit, cutting a loan-approval workflow from about 7.5 minutes to roughly 0.5–2 minutes.