Philipp Schmid
Staff Engineer in Developer Experience and Developer Relations at Google DeepMind, focused on helping developers build with Gemini, Gemma, and AI agents; previously a Technical Lead at Hugging Face.
Agent Skills Need Evals Before They Reach Customers
Google DeepMind staff engineer Philipp Schmid argues that agent skills should be treated as production dependencies: versioned, tested, and retained only when they measurably improve user outcomes. He says manual checks conceal failures in customer-facing agents, where skills can misfire, over-trigger, or quietly degrade plausible outputs; a lightweight evaluation harness should test routing and task completion repeatedly in clean environments, with and without the skill loaded.
Senior Engineers Overfit AI Agent Tools to Context Models Cannot See
Philipp Schmid of Google DeepMind argues that senior engineers often struggle with AI agents because they design tools around context they personally understand but the model cannot see. In his account, agent-ready systems need explicit tool schemas, semantic state, recoverable errors, eval-based reliability measures and disposable harnesses, because engineers are managing probabilistic behavior rather than controlling a deterministic flow.