The Rise of Runtime Intelligence: practical Lessons in Shipping Agentic Engineering Code to Production

Agentic engineering works remarkably well in controlled environments. Agents plan, reason, and adapt - until they are deployed into real production systems, where execution becomes dynamic, shaped by customer behavior, third-party dependencies, and infrastructure that can behave in surprising ways. 
Modern production stacks expose large volumes of telemetry. Interpreting that data, however, requires investigation across disparate systems to reconstruct what actually happened at runtime. For agentic systems that reason over code, this is a fundamental limitation: they need to know how the code behaves in production, right then and there. Does this function run at all? What flows would be impacted by changing it? Is it failing in a way that causes business damage? Under what specific conditions? 
Teams at the forefront of agentic engineering adoption - at companies like Monday.com, Cyera, and Drata - use a combination of tools to move quickly while maintaining production safety. In practice, when investigating issues such as errors, performance degradations, or CPU spikes, they rely on runtime intelligence: function-level data sent directly from production that contains a precise, end-to-end account of what occurred and why. 
This is exactly the level of information that AI coding agents need in order to operate effectively in complex production environments. Runtime intelligence gives agents a direct, function-level view of the code’s behavior, allowing them to identify core root causes and act with precision. It is not an extension of observability, but a foundational requirement for agentic systems that must function reliably in real-world production.