【#OpenDataSpaces】# Interview Part 3 is live! Break down silos across organizations and borders and turn data into capital. Part 3 explores how Open Data Spaces serves as #ContextLayer# for AI, including #DynamicOntology#. Watch the full version on🔗

A context layer inside the AI gateway learns from every question. It sees the data that comes back. It sees whether the answer held up. That idea was at the heart of my session at @AIconference yesterday, "What most AI gateways and goldfish have in common." I covered where your context layer can live and what opens up once it's inside: lower token spend, sharper agent accuracy, and read-write use cases teams had set aside. Let me know if you'd like to hear more!

Atlassian is becoming the context layer for your business. The formula: Information in. Intelligence out. 🤝 Connectors In: Pull signals from your entire toolchain. 🧠 The Graph: Data is mapped into the Teamwork Graph and compounds daily. 🔄 Context Out: Through our MCP server, that intelligence is pushed to whatever AI your teams already use.

🧠 One answer to "how do you give agents accurate business context?": auto-build ontologies and a knowledge graph from existing data, then serve it via MCP. An open-source project from AWS. Title: Context Ontology Accelerator (aws/context-ontology-accelerator) URL: A semantic context layer that gives AI agents validated business context. Three highlights stand out. 🔎 A Scan → Model → Serve pipeline Connect diverse data sources to discover schemas and ingest documents (Scan), induce formal ontologies and build a unified knowledge graph (Model), and expose it via VKG SPARQL federation and MCP (Serve). It derives semantic structure from existing data, no manual knowledge engineering. ✅ Consistency validated by reasoning engines HermiT and ELK validate ontology consistency, enabling rule-based checks from formal constraints. Agents query validated business rules instead of relying on memorized training data. 🏗 AWS-native and production-minded Deployed via AWS CDK, a VKG powered by Ontop, and namespace RBAC (owner/maintainer/data-steward/data-analyst). API design via Smithy, UI in React + Cloudscape. A solid foundation for running agents within validated context while keeping explainability. #KnowledgeGraph# #AIAgents#

In many discussions, I find that context is often being referred to as a synonym for more data, but more data doesn't necessarily mean more context. Organizational theory is a good way to think about context, which breaks enterprise knowledge into four categories: know-what, know-how, know-why, and know-who. Take a sales deal. 𝗞𝗻𝗼𝘄-𝘄𝗵𝗮𝘁 is the account plan, pricing, security questionnaires, and redlines. 𝗞𝗻𝗼𝘄-𝗵𝗼𝘄 is how to move the deal forward, like when to involve security and how to sequence the work.  𝗞𝗻𝗼𝘄-𝘄𝗵𝘆 is rationale, like which objections indicate real risk and which approvals are routine.  𝗞𝗻𝗼𝘄-𝘄𝗵𝗼 is the social knowledge, like who decides, who has handled a similar issue in the past, and who needs to be involved. Most of what AI can retrieve today falls in the know-what category. The rest is trapped in scattered conversations, unwritten routines, and informal human networks. We've spent the last 7 years at @Glean building toward this gap. Our context layer to connect know-what, know-how, and know-who, while inferring know-why from how work actually happens. An AI tool with access only to know-what can find the account plan but will fall short in telling you who to involve, which objection deserves attention, or what to do next. It’s the difference between knowing the account, and knowing how to move it forward.

AI doesn’t have an intelligence problem. It has a context problem. When business context is scattered across dashboards, documents, tickets and chats, users struggle to get fast, trusted answers and data teams stay stuck fielding ad hoc requests. See what it takes to close that gap with a unified context layer that helps AI deliver trusted answers, act autonomously and give teams back time. Go under the hood of Genie Ontology and see the approach in action with a product demo. Watch on-demand:

Genie Ontology works on day one, but the quality of its answers improves as the business context underneath it gets stronger. A practical path is to build that context layer by layer: ↳ Strengthen the data foundation ↳ Enrich metadata ↳ Model critical business semantics ↳ Curate trusted assets ↳ Govern access ↳ Evaluate and improve over time You don’t need to tackle everything at once. Start with one high-value domain, harden the definitions and sources that matter most, then expand as you learn. Read the guide: