Building Agents Backwards from Evaluation

In Part 1 of LLMs in the SOC, SentinelLABS examined the gap between cybersecurity benchmark scores and operational usefulness. In the months that have followed, we’ve observed those same design gaps in the flood of agentic systems hitting the market. This fail-fast development trend treats evaluation as a process that emerges after the proof-of-concept. In other words, teams build the system, demonstrate that it can work, and defer the harder task of establishing how reliably it performs and where it fails for a later date. 

This makes sense in the new world of rapid prototyping and AI experimentation. However, these systems are often brittle or only work until something in the environment changes or fails to generalize. This raises the urgent question, "How do I fix or improve this one behavior?" inside of a monolithic, non-deterministic pipeline.

For a system that will support security decisions, we believe the most important design choice is to make its performance measurable from the start. That means building the agent so that engineers can identify where an investigation goes wrong, test a correction, and verify that the change improves the complete workflow.