00:27What is context management?
DataHub Co-Founder & CTO Explains
95%
of AI projects fail to reach production
MIT, State of AI in Business Report, 2025
40%+
of agentic AI projects will be cancelled by 2027
Gartner, 2025
91%
of orgs plan to build or buy a context platform within 12 months
State of Context Management Report, 2026
Context management is the organization-wide capability to reliably deliver the most relevant data to AI context windows, enabling governed, enterprise-scale deployment of agents. Without it, AI agents hallucinate, return stale results, or fail entirely when moved from prototype to production. These resources define the category: from the foundational argument to the enterprise infrastructure that makes it real.
Context management gives AI agents secure, reliable access to enterprise data. Learn what it is and how to implement it.
Everyone’s building a context layer for AI. Most are building the wrong one. Here’s what enterprises actually need.
AI-ready data isn’t enough. Agents need AI-ready context: the definitions, runbooks, and institutional knowledge that give structured data meaning.
Benchmark report by independent research firm TrendCandy, commissioned by DataHub.
01
Context engineering gives individual teams tools to fill an agent’s context window. Context management gives the entire organization a shared, governed foundation that every agent can rely on. The distinction matters because context engineering doesn’t scale, and the gap shows up in production. These resources help you understand where one ends and the other begins.
Context engineering optimizes one agent. Context management scales trusted context across all of them. See how.
Context engineering needs your data engineering skills. Learn how metadata, governance, and pipeline expertise translate to building context for AI agents.
02
Every AI agent has a context window: the body of information it can process at one time. What goes in determines what comes out. For enterprise agents, a poorly filled context window is the most direct path to hallucination, inconsistency, and failed production deployments. These resources cover how context windows work and how to get the most out of them.
Learn what a context window is, how tokens work, why context limits matter for AI agents, and what it takes to manage context at scale.
Context window optimization techniques for AI agents, plus why upstream context quality determines the ceiling.
03
Context management cuts across data engineering, data governance, and AI development. In most organizations, no single team owns it — which is exactly why it breaks down. These resources examine who’s responsible for context management today, what’s changing, and whether a new role is emerging.
Context ownership can’t sit with one team. Here’s how data, analyst, and governance functions share it across a context platform.
AI can write SQL. Context management is the new skill set that keeps data analysts indispensable.
Context engineers build the systems that make AI agents reliable. Here’s what the role involves and why data engineers are a natural fit.
04
Context problems don’t announce themselves as context problems. They show up as agents that give stale answers, documentation that drifts from reality, and AI tools that work in demos but fail in production. These resources name the patterns and explain how to address them before they become blockers.
The common context problems breaking AI agents in production and how a context platform solves all five together.
AI agents can’t compensate for stale docs the way humans can. Continuous context is the missing maintenance layer. Here’s what it looks like.
AI agent memory architecture is mature. Memory quality isn’t. Here’s why governed context is the prerequisite for agent memory you can trust.
Context-aware AI agents need more than clever prompts. See why context-awareness is an infrastructure problem, and what production agents actually require.
05
Trustworthy context doesn’t just improve agent accuracy — it changes what’s possible. These resources cover the business case for context management, from third-party research on measurable ROI to a hands-on calculator that puts a number on it for your organization.
2026 IDC study on the measurable business outcomes DataHub Cloud’s context platform delivers for enterprise data teams.
Estimate the value of DataHub Cloud’s context platform for your organization based on your team size, data estate, and use cases.
Context platform ROI, measured. Five categories of hidden spend most organizations are already paying without knowing it.
Talk-to-data agents fail without context. Here’s what an LLM actually needs to query your warehouse and return the right answer.
Context management touches every team involved in building or using enterprise AI. The right starting point depends on your role.
Role
Start here
CDO / Head of Data
The business case and benchmark data to build organizational alignment.
Data Engineer
The technical foundations and implementation patterns.
Data Analyst
What context management means for the work you do every day.
AI / ML Engineer
What reliable context delivery looks like from the model side.
Data Architect
The phased approach to building context infrastructure at scale.
Context management is built incrementally. The teams that get it right start with a clear inventory of what they have, prove value with targeted pilots, and scale from there. These resources cover every stage of that journey.
01
Before you build anything, you need to know what context you have, where it lives, and what tools exist to manage it. Most AI projects skip this step and pay for it later. These resources cover the prerequisite work that separates production-ready context infrastructure from a demo that doesn’t survive its first real dataset.
A step-by-step breakdown of the four capabilities every production-ready context layer needs — and why you already have most of what it takes.
AI-generated documentation solves cold-start. Context propagation makes it stick. See how the two work together.
Four types of tools claim the “context management” label. Here’s how the landscape breaks down and what to evaluate.
02
Real data estates are messier than any architecture diagram suggests. These resources cover the implementation patterns and phased guides that hold up in practice for the teams building context infrastructure that has to survive contact with schemas that change, pipelines that break, and governance requirements that don’t bend.
A phased, practitioner’s guide to implementing an enterprise context layer on the metadata infrastructure you already have.
AI agent onboarding is the missing discipline behind production-ready agents. Why context engineering can’t do the job alone.
A context platform is infrastructure that unifies structured metadata with unstructured organizational knowledge into a context graph, and delivers that context to humans and AI agents through portals, APIs, and MCP servers. It handles what a data catalog handles, plus the documentation, decision logs, and institutional knowledge that give metadata its meaning.
DataHub Context Management Platform
Claude
Cursor
Cortex
Genie
CrewAI
LangChain
Agent Development Kit
Ask DataHub
MCP
Context Kit
APIs / SDKs
Skills
User Experience
Context Intelligence
Context Hub
Metrics
Lineage
Impact Analysis
Data Contracts
Data Quality
Access & Policy
Metadata
Knowledge
Embeddings
Memory
01
A data catalog indexes structured metadata so humans can find and understand data assets. A context platform goes further — unifying that metadata with unstructured knowledge and delivering it to AI agents at runtime, with governance enforced at the point of delivery. These resources explain the gap and what it takes to close it.
A direct comparison of what each tool does, where they overlap, and why the catalog alone isn’t sufficient for enterprise AI.
A context catalog makes metadata usable by AI agents and humans. Learn how it differs from a data catalog.
02
The context graph is the connective layer at the heart of a context platform. It links data assets, business definitions, ownership, and lineage into a unified semantic network, giving agents the relationships and meaning they need to reason (not just retrieve). These resources explain how it works and why it’s the foundation for production-ready AI.
Context graphs and knowledge graphs share the same shape. The real difference is scope and grounding, and it matters more than the vocabulary.
A metadata knowledge graph connects your data assets, pipelines, and meaning. Here’s what it is and why DataHub calls it a context graph.
From keynotes to hands-on demos, explore the concepts, architecture decisions, and real implementations that matter to teams building production AI.
Context management doesn’t exist in isolation. These resources define the concepts around it and explain how each one fits into the broader picture.
RAG is a retrieval pattern. Context management is the infrastructure that makes it work at scale. Learn the difference.
What each tool actually does, where they overlap, and why neither one covers context management on its own.
Technical metadata says what data is. Business context says what it means. Learn why that gap breaks AI agents.
Data prep made data usable for analysts. Context preparation makes it usable for agents. Why both matter, and why most enterprises have only done one.
Why prompt engineering alone isn’t enough for production AI, and what the shift to context engineering actually requires.
There’s more than one way to connect your agents to trusted context. Context can reach your agents through the Model Context Protocol (MCP), native integrations, and SDKs. These resources cover some of the connection options available to get trusted context flowing to your agents.
The DataHub Analytics Agent is the first open-source talk-to-data agent built on a context platform. Here’s how it works and how to deploy it.
Snowflake agents are only as smart as the context they have. Learn how DataHub’s Agent Context Kit adds business definitions, lineage, and data quality.
Pinterest built a context intelligence layer that indexes years of analyst query history as searchable business intent, powering an Analytics Agent that delivers trusted answers in minutes.
Netflix unified data, ML, and software assets across the company, enabling self-serve discovery, faster impact analysis, and the context infrastructure to power agentic AI at scale.
More teams build context management on DataHub than anywhere else — from open-source contributors to enterprise deployments across every major industry.
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GitHub stars
15K+
community members
3K+
Organizations
1.5K+
data and AI leaders at CONTEXT 2025
Context management is the organization-wide capability to reliably deliver the most relevant data to AI context windows, enabling governed, enterprise-scale deployment of agents. It covers how context is sourced, enriched, maintained, and delivered across every AI application in an organization — not just within individual pipelines or tools. Without it, agents operate on fragmented, stale, or ungoverned information, and production AI initiatives stall. Read our article Context Management is the Missing Piece in the Agentic AI Puzzle for a detailed overview.
Context engineering is the prompt-construction discipline that decides what goes into a model’s working memory on any given turn: what to retrieve, how to structure it, what to include or exclude. Context management is the upstream question of whether the context being engineered is trustworthy, governed, fresh, and semantically coherent in the first place. Both are necessary. Context engineering operates at the prompt layer. Context management operates at the infrastructure layer. Read our comparison guide Context Engineering vs. Context Management for more information.
A context window is the body of information a model can process in a single request. For enterprise AI agents, what fills that window determines the accuracy of every output. An agent working from stale documentation, ungoverned definitions, or incomplete lineage will produce confident wrong answers. Context management ensures the right information (meaning information that is relevant, reliable, and authorized) is available at query time. For a deeper dive, read What is a Context Window?
A knowledge graph maps facts and relationships between entities in a general, domain-agnostic way. A context graph maps those relationships specifically to an organization’s data assets, pipelines, AI agents, and governance policies. Knowledge graphs optimize for meaning and inference. Context graphs optimize for operational trust, connecting what data means to who owns it, where it came from, and whether it can be used. For more information on the differences and similarities between the two, read our comparison guide: Context Graph vs Knowledge Graph.
LLM grounding is the practice of anchoring a model’s outputs to verified, real-world data rather than relying on what the model learned during training. An ungrounded model generates responses from pattern-matching across its training corpus. These outputs are plausible, but not necessarily accurate or current. A grounded model generates responses by querying a trusted external source at runtime and citing what it finds.
Context management enables grounding at enterprise scale by ensuring the sources an agent queries are accurate, governed, and semantically coherent. DataHub’s context graph provides the grounding layer: certified datasets, end-to-end lineage, business definitions, and quality signals that agents can retrieve and reason over. Without context management, grounding degrades, resulting in agents that pull from whatever is available rather than whatever is trustworthy.
RAG (Retrieval-Augmented Generation) is a retrieval pattern that pulls relevant documents into a model’s context window at query time. Context management is the infrastructure that determines whether what gets retrieved is accurate, governed, and current. RAG operates at the application layer. Context management operates at the foundation. Without it, every RAG implementation is only as trustworthy as the data it retrieves from. For a deeper dive, check out our guide on RAG vs. Context Management.
Model Context Protocol (MCP) is an open standard that lets AI agents request context from external systems at query time, rather than relying on hard-coded connections or bespoke retrieval pipelines. DataHub’s MCP server exposes the full context graph through this interface, returning metadata, lineage, ownership, and data quality signals to any MCP-compatible agent at runtime. The server sits behind an enterprise API gateway, creating a single control point for authentication, authorization, and audit logging. Agents get governed access to trusted context. Data teams get a complete record of what was accessed and when. Read our Practical Guide to MCP Context Management to learn more about how it works.
DataHub ingests metadata from 100+ sources, including data warehouses, BI tools, ML platforms, dbt, and more, and builds a unified context graph connecting data assets to business definitions, ownership, lineage, quality signals, and institutional knowledge. That context is then delivered to AI agents at query time via the DataHub MCP server with governance policies enforced at the retrieval layer before context reaches the model.
Basic RAG retrieves documents and passes them to a model without governance, provenance, or access controls. It answers the question “what is relevant?” without asking “is this authorized?” or “can this be trusted?” Secure agentic architecture enforces fine-grained access controls at the retrieval layer (meaning before context reaches the model) and attaches provenance metadata to every piece of context delivered. If you need to audit what an agent used to reach a decision, basic RAG gives you nothing. A governed context layer gives you a complete trail.
Yes. DataHub Core is fully open source under the Apache 2.0 license, with a community of 15,000+ active practitioners. DataHub Cloud adds managed infrastructure, advanced AI and governance features, and SLA-backed support for enterprise deployments. Organizations can start with Core and move to Cloud as their context management requirements scale. For more on the differences between them, visit DataHub Cloud vs. Core.
