The context layer is themost viral thing in 2026
Well, after Openclaw
Your foundation model says they are a context layer. Your semantic layer vendor says they're a context layer. Your knowledge graph company, your data warehouse company, your data integration company, your system of record — all context companies now.
And you're stuck with the questions nobody's answering:
- Is a context layer just a data catalog with AI features?
- Do I need a knowledge graph, an ontology, a semantic layer — or all three?
- How do I go from not knowing where my data lives and 500,000 undocumented assets to a working context layer without a 2-year project?
- What does a context layer actually look like in production?
- Who owns the context layer — the data team, the AI team, or the platform team?
Product Launches
The Context Engineering Studio
AI bootstraps 90% of your context layer. Your team adds the rest — business logic, edge cases, knowledge only they have. Simulate. Refine. Ship to any execution engine.

Context Agents
9-12 months of enrichment work. Done in minutes. Nine AI agents solve the cold start problem the moment you ask.

Traces & Observation Loops
Agents get things wrong. Traces show exactly where. Corrections flow back into context automatically. Smarter every week — without touching the model.

Agenda
Enterprise Data Graph
Where does context actually come from? We’ll show you the living map. And what happens when AI agents finally have access to it.

Context Agents, Live
Nine AI agents that write, maintain, and continuously evolve the documentation your team never did. Watch them outperform humans on documentation quality.
Conversational Search, Reimagined
Ask questions about your context like never before and learn what it takes to build accurate, performant context retrieval.
The Context Ecosystem, Connected
The Enterprise Data Graph is the backbone of the context ecosystem. Partners have built on it. AI systems read from it. Watch what happens when context is shared by design.
Context Engineering Studio
Engineer, test, and ship the enterprise knowledge that makes AI agents reliable.

Bootstrap Context Repos
AI bootstraps 90% from SQL patterns, lineage, and usage signals. Domain experts add the last 10%: business logic, exceptions, the tribal knowledge only they have. Versioned. Bounded. Portable to any execution engine.
Simulate & Test Context
Turn your BI dashboards into an automated eval suite. When a test fails, the studio pinpoints what’s missing and suggests a fix. Know when your agent is ready before your users find out it isn’t.
Observe & Improve
Every production interaction is a signal. Corrections become context updates. Correct answers become regression tests. Agents get more reliable every week, without the model changing.
Context Lakehouse
The first knowledge architecture built for a world where AI is the primary producer and consumer of context.

Map the Knowledge Graph
Domains, semantic models, lineage, policies, quality scores. All connected. All live. Watch what changes when an agent knows what an asset means, where it came from, and whether it can be trusted.
Own Your Intellectual Property
Context is IP. The knowledge your teams have built about your data belongs to your organization. We'll show you what it means to store, version, and own it on open standards. Yours.
Deploy Context to Agents
MCP for governed queries. A2A so agents write context back. SQL for analytics. Every interface an agent needs. Watch context move from question to answer, in production.
Introducing the AI Context Ecosystem
We're bringing together the entire AI context ecosystem, for the first time ever.

App Framework
See how developers, partners, and customers can build connectors and apps using Atlan's App SDK.
Context Layer Partners
See how partners across observability, orchestration, security, governance, and business systems come together to form the AI Context Ecosystem.
Policy Context Layer
See partner demos from Cyera and Immuta on how they built their own apps on Atlan to connect the Policy Context Layer.
This is for you if
You're a CDO, VP Data, VP AI, or platform lead
trying to figure out who owns context in your organization
You have 500,000+ data assets
and no idea how to document them for AI without a multi-year project
You're building AI agents and they keep getting things wrong
— not because the data is bad, but because the agent doesn't know what the data means
You've heard "context layer" from every vendor this year
and you still can't picture what one actually looks like
You survived the pilot
but now you need to scale to 10 agents — and nothing is shared
If any of these sound familiar, this is the one event to show up for this quarter.
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