【#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🔗

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.

Are you fully agent pilled? We made a plan just for you. Introducing Builder: unlimited external-agent calls for $10/month. Run Cursor, Codex, and Claude Code side by side with MagicPath as your visual context layer as you build. Your design roundtrip is complete.