Three-person data team, a queue of weekly requests, and every analytical question outside predefined dashboards stuck in someone's inbox — that was LangChain's BI era. 🔍 The team decided to change. What they needed was a platform unifying dashboards, notebooks, and conversational interfaces with native AI agent capabilities. They chose Hex and designed a five-layer context architecture: dbt data model definitions, a semantic layer, workspace guides, endorsements as trust signals, and GitHub integration. The migration was complete in six weeks with 100% company adoption. The transformation's core wasn't technical — it was about explicitness. Rewriting a weak definition like "account_status: The status of the account" into a full description covering lifecycle states, default filters, and reporting conventions alone changed how reliably the agent answered questions. Key metrics — ARR, pipeline, customer health — were given single authoritative definitions in the semantic layer. Endorsements pointed agents to canonical sources wherever multiple assets addressed the same concept, preventing confusion before it could happen. Today, every function — marketing, product, sales, customer engineering — runs its own analysis without routing through the data team. Monthly agent conversations total roughly 2,200, processing 40x the volume the three-person team could handle manually. The team's role has shifted from "answer every question" to "design the system that lets others answer questions." LangChain's detailed account of this journey, "How LangChain Built an Agent-First Data Stack," makes the case — in concrete numbers and design principles — for what a data team looks like in the agent era. #DataStack# #AIAgent#

Your AI agents are only as good as the data behind them. Fragmented data, inconsistent definitions and weak governance can hold marketing teams back. Learn how leading organizations are preparing for the agentic enterprise in our latest report – the 5th edition of the Modern Marketing Data Stack.👇

CoinGecko API is now the most complete AI-native crypto data stack for builders & AI agents. 🤖 Over the past few months, we released 4 AI tools to help you build, ship, & analyze faster: • CLI • CoinGecko Skill • x402 Endpoints • MCP Server Here's how they work 🧵

TechCrunch reported that Encord is working with Zander Labs to test a new physical-AI data pipeline: capturing first-person video and brain-wave signals while humans perform manipulation tasks. This is not about “mind-controlled robots.” The goal is to make training data more informative. Brain signals could help mark intent, error, surprise or cognitive load — context that video alone may miss. Encord’s broader physical-AI data stack also includes in-field collection, teleoperation, multi-sensor data, annotation and deployment feedback. Robots need structured examples of hands, tools, objects, motion, timing and failure cases to learn the physical world.

Governance isn't slowing innovation, it’s enabling it. Snowflake CMO Denise Persson unveils the 5th Modern Marketing Data Stack report. Get the blueprint for the agentic enterprise. 👉🏻