--- title: "The GitHub for Enterprise Context: How Atlan's Context Repos Bootstrap, Test, and Ship Agent-Ready Context" url: "https://atlan.com/demos/the-github-for-enterprise-context-how-atlans-context-repos-bootstrap-test-and-ship-agent-ready-context/" excerpt: "Your agents run everywhere. Their context should too. The Context Engineering Studio is how Atlan ships context to any tool, warehouse, or framework." description: "AI agents don't live in one place. They run in your IDE, in Cursor, in Claude, in ChatGPT, in Snowflake Cortex Analyst, in Databricks Unity Catalog, in whatever framework your team standardizes on. The context they need to work has to travel with them. Atlan's Context Engineering Studio is where you compose, iterate, and distribute that context layer. The Context Repository (GitHub for context) holds the semantic models, skills, and soul.md files your agents run on. Simulations test agents against synthetic scenarios before they face customers. Observability captures every production trace and surfaces the exact context fixes that stop failures from repeating. Every artifact ships out: deploy semantic models to Snowflake Cortex Analyst, Databricks Unity Catalog, GitHub, S3, or any framework that speaks MCP. Iterate inside Atlan. Ship the context wherever your agents run." format: "Video" duration: "PT4M20S" video: "https://videos.ctfassets.net/nwa1c00rtgxb/1QDDWO4n5oagZaYYum0v1q/d8cab58fa5b16dfc7e12e8a36092ba23/Context_Engineering_Studio.mp4" thumbnail: "https://images.ctfassets.net/nwa1c00rtgxb/2wUDyUWO3YRpgg6LXkfAOQ/403bd9d4dc3fc8801f271bc3c256a400/the-github-for-enterprise-context.webp" content_purpose: ["Product Overview"] target_persona: ["General", "Data Engineer", "Data Analyst", "Data Scientist"] journey_stage: ["S1 - Prospecting", "S2 - Discovery", "S3 - Solution Design", "S4 - Business Case", "C1 - Onboarding", "C2 - First Value", "C4 - Expansion", "C3 - Adoption"] use_case_context: ["Training"] product: ["Context For AI - Context Engineering Studio"] published: "2026-05-04" updated: "2026-06-02" content_type: "video transcript" transcript_source: "sheet" --- # The GitHub for Enterprise Context: How Atlan's Context Repos Bootstrap, Test, and Ship Agent-Ready Context Transcript of the video at https://atlan.com/demos/the-github-for-enterprise-context-how-atlans-context-repos-bootstrap-test-and-ship-agent-ready-context/ Now let's jump into the product and show you a little bit about how Atlan's Context Engineering Studio works. Now, if you're, if you're someone who doesn't like using IDs or doesn't like using CLIs, we also offer you to do the same workflow within Alan itself. So you can define the same natural language prompt and we can build this agent through a context repository. But you might be wondering what's a context repository? Let me show you one. So a context repository is nothing is similar to how a, you have a GitHub repository for code. A context repository is a repository for context. Now, what a context repository has is everything that your agent needs to be able to do its task, like we just saw. It has all of your semantic models, it has all of your skills that you need to do an operation. It has, and it has something called a Solar MD file. So let's see what a so MD file is. It's what gives Maya the unique identity of a make context, context center agent. What's a skill here? We have something called a loop closing skill. A loop closing skill is what allowed it to do the follow up in the first place, even after the issue was resolved, because that's what's unique about our contact center. Our contact center, and how we run our customer operations. It has all of our semantic models, which allow the agent to understand which columns to query, which tables to query. Now all of this comes into our context letter. Finally, imagine Maya when she joined a new job, she was not first put into doing all of these calls. What? Atlan offers is a way to simulate, just like Maya would go through training, she would go through mock calls with her seniors, with her managers. Atlan runs simulations of different scenarios so that you know how your agent is performing even before you're deploy to end customers. This gives builders a sense of confidence of how the agents are performing in a synthetic simulated environment. The reason we are able to do this so well is because. Atlan has a deep contextual understanding of both your data and business graph, and all of that is leveraged to be able to create all of these scenarios for simulations. One of the core beliefs that we have in Atlan is of openness, interoperability, and that's where a context repo becomes super useful. We have something called a Lakehouse, which Warren will talk further about. We also have our Atlan, MCP, which we showed you, which is compatible with with Chad GBT, with cloud, with Gemini, with any of your coding tools. Also, with all of the major AI frameworks. We also can help you deploy your semantic models to things like Snowflake Cortex Analyst, AWA Databricks Unity Catalog, and we're building support for you to be able to send these files to GitHub and your S3 locations as well. For any agent operating in the real world, the learning does not stop at them, doing their job. It comes through daily feedback that comes through their managers, through their peers. That's why Aline has observability. What we do in observability is that every single trace that the agent is actually performing is captured here. And once it's captured, it creates a feedback loop for you to continue to improve your context level. What we also offer is a way for you to deep dive into the trace to understand what the agent was doing, what parts of the context that it's touching, what tools it's using, and for anyone debugging an agent, it can also look at and understand what the reasoning trace was. Now looking at hundreds of thousands of traces is really, really hard. What Atlan does is that it actually watches over every single one of your traces and identifies patterns that you can easily go and fix to improve your context. So for example, just like Maya would have sometimes associated the wrong tier to a customer and leading to a bad refund. Similarly, if your agent does the same operation, Atlan's observability identifies them and gives you a way to improve that con, improve the context so that it doesn't repeat. Building a self-improvement flow that can help you improve the quality of your context as more interactions happen.