Most teams still evaluate AI agents one at a time, checking a single agent against a benchmark score. That works fine in isolation. It stops working once agents operate across the full SDLC, from planning to coding to review to deployment. In a connected pipeline, each agent's output feeds the next agent's input, so evaluation has to account for the whole system, not just individual components. This makes evaluation a platform engineering challenge as much as a data science one.

In this session, Glyn Darkin, ClearRoute's Global Head of Delivery, walks through a practical approach to this challenge. Watch the recording to learn what to measure at each stage of the agentic SDLC, how to connect evaluation signals across the pipeline, and how to build evaluation into your platform as a core capability.

If your current approach to agent evaluation is a set of standalone tests, this session gives you a clear path to something more scalable.

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