Agents
Our internal AI agent was a brute-force nightmare. 🛑 It burned 40,000 tokens, took 2 minutes, and only hit 68% accuracy just to answer a si…
Our internal AI agent was a brute-force nightmare. 🛑 It burned 40,000 tokens, took 2 minutes, and only hit 68% accuracy just to answer a single question. Why? Traditional databases are built for human
Our internal AI agent was a brute-force nightmare. 🛑 It burned 40,000 tokens, took 2 minutes, and only hit 68% accuracy just to answer a single question. Why? Traditional databases are built for humans. Agents don't have that context, so they just guess inside expensive LLM prompts. We realized we couldn't keep pushing raw data into the LLM. We needed to bring the context closer to the data itself. That led us to build Pinecone Nexus, a knowledge engine that compiles the exact context a machine needs before it hits the LLM. 🧠🏗️ When we moved our agent to Nexus: 📉 Tokens: 40k → 2k (90% drop) ⏱️ Latency: 2 mins → <500ms 🎯 Accuracy: 68% → 90%+ Our CEO @ashashutosh sat down with @a16z General Partner Peter Levine to share our customer zero story and how we're rewriting the AI stack. Stop burning tokens on search. Listen here: 👂Spotify: https://open.spotify.com/episode/2RAlltowItb13rQeht5DKa?si=d9742698524a4553 👂Apple: https://podcasts.apple.com/us/podcast/from-vector-databases-to-knowledge-engines-the-next/id1740178076?i=1000766256620 Media
Source: Pinecone (X) | 2026-05-11