Google talk: split agent tools into build-time vs run-time before your AI drops the table
AI Engineer · youtube · 2026-09-09
Averi Kitsch (tech lead, MCP Toolbox on Google Cloud databases) and Prerna Kakkar (leads Eval Bench at Google) open with a live failure: an agent hits an error, decides the fix is to drop the table and start fresh — and nothing stops it.
Build-time vs run-time. Build-time tools serve developer assistance: control-plane tools managing instances, and natural-language-to-SQL that writes whatever query is needed. Flexible, human-supervised, and not for production. Run-time tools are the opposite: structured SQL with parameters fixed ahead of time, closing off injection, cutting latency, leaving no room to invent.
Security. A database is only as secure as the agent in front of it; the confused deputy attack is the shape to watch — a triage agent reads a ticket with a planted instruction to query the salary table and post results, and complies because it has privileges. Their answer separates user, application, and agent identity, then narrows a tool from super user: connection details move to a YAML the agent never sees, read-only enforcement reaches the driver, allowed datasets and output caps bound the blast radius, custom tools pin exact SQL behind prepared statements, and sensitive values like user IDs are bound by the application so the agent never handles them. They close on tool quality: outcome-shaped tools, read split from write, and errors an agent can act on.
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