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Context Components · AI Context Platform

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.

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The missing middle

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.

Mine it once. Engineer it. Serve it through whichever door an agent knocks on.

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.

EP 05How Do Graph Databases and the Context Layer Fit Together?Wed, Aug 19 · 11 AM ET
Emil Eifrem and Prukalpa Sankar agree on the problem: enterprise AI fails when agents can't navigate the relationships that give data meaning. Emil argues graph databases are the foundation — the layer that maps how entities and relationships connect. Prukalpa argues: graph databases store structure, but the context layer carries meaning, governance, and lineage. They join Austin to work toward a shared picture of what enterprise AI actually needs.
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How Do Graph Databases and the Context Layer Fit Together?
THE AI CONTEXT GAP

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.

Knowledge

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

Expertise

How do you investigate this?

Validate the premise, check seasonality, then look at launches and weather before naming a cause

Norms

Who's asking, and what can they see?

Store manager sees their store only; VP Ops sees the whole chain

CONTEXT COMPONENTS

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 Ontology
ONTOLOGYENTITIES + RELATIONSHIPSholdsreceivescontainsCustomerAccountInvoiceChargeCLASS HIERARCHYis-aRoamingChargeRULES — ALSO PART OF THE ONTOLOGYRULE · a Charge that occurredDuring aNetworkEvent status = outage ⇒ refundEligible

ONTOLOGY

ENTITIES + RELATIONSHIPS

Customer
holds
Account
receives
Invoice
contains
Charge

CLASS HIERARCHY

Charge
is-a
RoamingCharge

RULES — ALSO PART OF THE ONTOLOGY

RULE · a Charge that occurredDuring a NetworkEvent status = outage ⇒ refundEligible
HOW IT WORKS

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.

Watch Atlan engineers bootstrap, test, and deploy enterprise context on real data — from cold start to production-ready agents.

The Enterprise Context Layer Architecture in ActionWed, Aug 26 · 11 AM ET
The teams winning with AI are not the ones with the best models. They are the ones building context that compounds. Watch Atlan mine knowledge from your existing stack, build and certify a context layer through a development lifecycle, and deploy it to any agent platform.
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Isabella Steains
Isabella SteainsStrategic Solutions Engineer

Build the context once.
Every agent inherits it.