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Context Layer for AI Agents: What It Is and the Three Tests It Has to Pass
A context layer for AI agents has to be shared, stay current, and prove it works. Here are the three tests yours should pass.
AI Agent Context Window Management: Why the Window Fills Before the Agent Finds the Answer
AI agent context window management starts before the window. See why agents burn tokens on discovery, and how resolved context fixes it.
Data vs Metadata: Key Differences for Data Teams
Data vs metadata: the line moves depending on what you're asking. A warehouse-level guide to the difference and why it matters.
What Is Metadata? Definition, Types, and Examples for Data Teams
What is metadata? Learn the definition, the main types of metadata, real examples from data teams, and why it matters for AI.
How to Run a Data Lineage Impact Analysis (and What to Do With the Result)
Data lineage impact analysis answers what depends on this. Here's how to run one, how far to trace, and what to do with the result.
Introducing DataHub Cloud v2.3
DataHub Cloud 2.3 brings Context to public beta, with evals, agent feedback, and metrics in lineage so you can trust what your agents say.
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What is CONTEXT 2026? DataHub’s Annual Context Management Summit
CONTEXT 2026 is a free context management summit on November 4. See how teams build, govern, and monitor context for AI agents.
Governance at Scale: August 2026 Town Hall Highlights
August 2026 town hall recap: the DataHub Metrics Catalog ships, Trustpilot governs 500,000+ assets, and Otto enters beta.
Data Lineage Best Practices: Six That Decide Whether Anyone Uses Your Graph
Six data lineage best practices, including the two most teams skip: giving lineage context and delivering it where people already work.
Data Lineage Tracking: Why Coverage Stops at the System Boundary
Data lineage tracking is only as good as its coverage. Why capture that stops at one platform's boundary hides what breaks downstream.
AI Agent Context: The Four Layers Every Agent Needs (and How Each One Fails)
AI agent context is more than what fits in the window. See the four layers agents need, and how each fails silently in production.
What Shipping an AI Agent to 16,000 People Taught Me About Reviewing One
“Welcome to DataHub! Hey, we’re working on this Otto agent, can you help us launch it?” That was how I started my summer internship…