Philipp Schmid
Senior AI Relations Engineer at Google DeepMind, focused on AI developer experience and helping developers build with Gemini and other AI models. Previously a technical lead at Hugging Face, where he worked on cloud partnerships and AI deployment.
Gemini Managed Sandboxes Give Agents Persistent Cloud Environments
Philipp Schmid of Google DeepMind argues that AI agents should run in managed cloud sandboxes rather than on a developer’s machine. In his account of Google’s Gemini API, the Interactions API represents work as a sequence of steps, while the Antigravity agent runs code, uses tools and retains files in a persistent environment started with an API call. Schmid’s case is that Google can handle the agent loop and infrastructure, leaving developers to provide the request, tools and working context.
Managed Sandboxes Replace Agent Tool Registries With Skills and Filesystems
Google DeepMind’s Philipp Schmid argues that agent builders should stop encoding anticipated workflows in custom functions and instead give capable models a managed sandbox, general-purpose tools, clear instructions and tightly defined access. In his three versions of a GitHub pull-request reviewer, Python loops, tool schemas and registered functions give way to an `AGENTS.md` file, skill files and a remote environment that handles execution, state and credential injection. The work that remains with teams, he says, is setting behavior, permissions and evaluation—not maintaining orchestration that improving models can replace.
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.