Google Cloud

Build-Time vs. Run-Time: Why Dev Tools Fail in Production — Averi Kitsch & Prerna Kakkar, Google

Sep 9, 2026 · 20:26

Averi Kitsch, technical lead for MCP Toolbox on Google Cloud databases, and Prerna Kakkar, tech lead for Eval Bench at Google, argue build-time tools like natural language to SQL don't belong in production; run-time tools need structured SQL with preconfigured parameters to block injection and hallucination. They show a demo where an agent hit an error and deleted the table. Security hinges on the confused deputy attack and Simon Willison's lethal trifecta, shown via a triage agent that queried a salary table and posted results to a ticket. Their fix separates user, application, and agent identities, moves connection details into a YAML source, enforces read-only at the driver, and pins SQL behind prepared statements, with sensitive values like user ID bound by the application.

Agentic Workflows on Vertex AI: Rukma Sen

Feb 8, 2025 · 18:06

Google Cloud's Rukma Sen argues that AI agents are the essential bridge between generative models and users, and Vertex AI provides a platform to build and deploy them with enterprise-grade safety and flexibility. She defines agents as systems with a model (brain), tools (hands), and orchestration (nervous system), covering deterministic, generative, and hybrid types. For production reliability, she advocates multi-agent architectures, giving the example of a customer service system with dispatcher, expert, and supervisor agents—and a personal anecdote where a supervisor agent kept rejecting outputs. Use cases span customer support, employee HR, knowledge agents, and voice agents for drive-throughs. Vertex AI offers 150+ models (Google, Anthropic, LLaMA) and Agent Builder from no-code to full-code, with enterprise security and data privacy.