Building a compliance audit agent using Nebius Agents Blueprint

AI agents often encounter production failures not due to the model itself but because of the supporting system, which may include issues like incorrect data retrieval, lack of current information for queries, unsustainable costs, lack of traceability, and untested behavior. The text discusses a case study involving the development of Sentinel, a regulatory compliance audit agent, to illustrate the process of making an AI agent production-ready by focusing on reliability, observability, and economic feasibility. The study follows Sentinel through four configurations—Prototype, Grounded, Optimized, and Production—each addressing different bottlenecks such as data freshness, inference economics, runtime, and decision-making. The production configuration achieved the best results by ensuring complete traceability and conducting adversarial testing before launch, ultimately focusing on delivering actionable decisions rather than just findings. The narrative emphasizes the importance of infrastructure and continuous improvement for successful large-scale AI deployment, highlighting the Nebius Agents Blueprint as a tool for identifying and optimizing system bottlenecks.

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