{"id":12379,"date":"2026-06-02T12:15:18","date_gmt":"2026-06-02T19:15:18","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/cosmosdb\/?p=12379"},"modified":"2026-06-02T03:03:10","modified_gmt":"2026-06-02T10:03:10","slug":"azure-cosmos-db-agent-kit-now-battle-tested-for-ga","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/cosmosdb\/azure-cosmos-db-agent-kit-now-battle-tested-for-ga\/","title":{"rendered":"Azure Cosmos DB Agent Kit now battle tested for GA"},"content":{"rendered":"

Back in January, we shipped the Azure Cosmos DB Agent kit in preview<\/a> with 45 rules and a hypothesis: if we package Azure Cosmos DB expertise into a format that AI coding agents understand, developers will stop making the same expensive mistakes. That hypothesis held up. What surprised us was how much the rules themselves needed to evolve once we started systematically testing them.<\/p>\n

Today the Agent Kit is generally available<\/a> . It now contains 120+ rules across 12 categories<\/strong>. But the number that matters more: we’ve run over 200 automated test iterations where AI agents build real applications from scratch using these rules, and we’ve fixed every gap those tests exposed.<\/p>\n

\"Promotional<\/a><\/p>\n

Why We Spent Four Months on Testing Infrastructure<\/h2>\n

We could have just kept adding rules. That’s what most knowledge bases do \u2014 accumulate content and hope it’s correct. Instead, we built something we hadn’t seen anyone else do for agent skills: a closed-loop testing system that runs the rules through real code generation and checks the output.<\/p>\n

The setup works like this. We have five application scenarios \u2014 an e-commerce order API, a gaming leaderboard, an IoT telemetry pipeline, a RAG chat app, and a multi-tenant SaaS platform. For each scenario, we define an API contract (what endpoints exist, what they return, what edge cases they handle). Then we let GitHub Copilot generate the entire application with our skill loaded, spin up the Azure Cosmos DB Emulator<\/a> in CI, build the app, run it, and hit it with a full test suite covering API behavior, Cosmos infrastructure setup, and data integrity.<\/p>\n

When tests fail, we examine the cause. In some cases, the rule itself is wrong. In others, the rule is technically correct but vague enough that agents interpret it differently. Sometimes the issue exposes a gap: an Azure Cosmos DB behavior that had not yet been documented. In every case, we update the rule and run the batch again.<\/p>\n

We don’t run each scenario once. We run it 5+ times per language to get statistical confidence. One passing iteration might be luck. Five passing iterations means the rule is solid.<\/p>\n

Some results that gave us confidence to ship GA:<\/p>\n