Every agent, working from
the same understanding.
An AI context platform supplies every kind of context an agent needs, defined once and served to all of them. Atlan mines it from the systems you already run, governs it like code, and serves it through MCP, SQL, APIs, and graph.
Diagram of an AI context platform. Four classes of source system on the left feed one governed layer holding the data graph, semantics and ontology, skills, and memory. A protocol row of MCP, SQL, API, and graph serves copilots, agents, analytics, and enterprise apps on the right.
Trusted by AI-forward enterprises
"Atlan is our context operating system to cover every type of context in every system including our operational systems. For the first time we have a single source of truth for context."
Sridher Arumugham
Chief Data Analytics Officer, DigiKey
"We built a revenue analysis agent and it couldn't answer one question. We started to realize we were missing this translation layer. We had no way to interpret human language against the structure of the data."
Joe DosSantos
VP Enterprise Data & Analytics, Workday
"AI initiatives require more context than ever. Atlan's metadata lakehouse is configurable, intuitive, and able to scale to hundreds of millions of assets."
Andrew Reiskind
Chief Data Officer, Mastercard
"We're focused on how we can scale context development as much as possible, and where can we leverage Atlan AI to build the most robust definitions across our data estate and make sure we're propagating it upstream and downstream."
Takashi Ueki
Head of Enterprise Data & Analytics, Elastic
WTF IS THE CONTEXT LAYER
A bi-weekly live series for AI leaders and builders. One burning question per episode, an open AMA floor, and guests who've actually built context infrastructure.
- What's the difference between graph databases and context platforms?
- Where should an ontology live vs a knowledge graph?
- If graph databases have existed for years, why isn't the context problem solved already?
One question.
Three kinds of context.
Even a simple business question needs several kinds of context at once. Knowledge is what things mean. Expertise is how the work gets done. Norms are what the person asking is allowed to see.
Why is drive-through time up this week?
Question It Raises
Context Type
Answer It Needs
What does "drive-through time" mean?
avg_dt_secs, not the POS number Finance uses — and the week runs Monday to Sunday, store local time
How do you investigate this?
Validate the premise, check seasonality, then look at launches and weather before naming a cause
Who's asking, and what can they see?
Store manager sees their store only; VP Ops sees the whole chain
No agent runs on
one kind of context.
Meaning, facts, trusted data, procedure, and precedent. Each is a different structure, and answering one real question usually takes several of them at once.
What are the business’s entities, relationships, and rules?
Entity types, named relationships, and the logic that has to hold. It carries no data of its own, which is the point: it is the blueprint a knowledge graph gets filled in against.
Explore OntologyONTOLOGY
ENTITIES + RELATIONSHIPS
CLASS HIERARCHY
RULES — ALSO PART OF THE ONTOLOGY
Your organization’s shared brain,
mined from the systems you already run.
Context reverse-engineered from the systems you already run.
If you want to know how a business thinks it runs, read its documentation. If you want to know how it actually runs, watch its systems. Atlan reconstructs meaning from what is already there rather than asking your team to write it from scratch.
The same lifecycle software gets, applied to meaning.
Context stops being scattered prompts and hidden instructions and becomes a versioned, testable asset with an owner and a change history. AI drafts and tests. A human decides what becomes canonical.
The same governed meaning, through whichever interface an agent speaks.
Agents knock on different doors. Some speak MCP, some call an API, some run SQL, some traverse a graph, and many assemble several at once. A context layer that picks one protocol has already lost half its consumers.
See the context layer, built live.
The components of a context layer, one at a time.
The enterprise context layer, 53+ resources
The full resource hub: what a context layer is, why agents need one, and how teams put one into production.
Context graphs: $1T opportunity. Four positions. Zero consensus.
Bob Muglia, Karthik Ravindran, Tony Gentilcore, and Prukalpa Sankar debate who should own the context graph.
Context Agents: the team that writes your context
Nine specialists that read lineage, SQL, and usage, then turn them into descriptions, metrics, and ontology.
Context Engineering Studio
Where a draft definition gets tested against real history, reviewed by its owner, and versioned like production code.
84% invest in AI. 17% reach production. Here is the gap.
550+ data leaders on what separates the teams that scale from the ones that stall.
The enterprise context layer, 53+ resources
The full resource hub: what a context layer is, why agents need one, and how teams put one into production.
Context graphs: $1T opportunity. Four positions. Zero consensus.
Bob Muglia, Karthik Ravindran, Tony Gentilcore, and Prukalpa Sankar debate who should own the context graph.
Context Agents: the team that writes your context
Nine specialists that read lineage, SQL, and usage, then turn them into descriptions, metrics, and ontology.
Context Engineering Studio
Where a draft definition gets tested against real history, reviewed by its owner, and versioned like production code.
84% invest in AI. 17% reach production. Here is the gap.
550+ data leaders on what separates the teams that scale from the ones that stall.
Build the context once.
Every agent inherits it.
