1,000 AI agents, 0 LLM calls? @googlecloudtech

Uploaded June 2026 | Updated September 2026, 3 weeks ago

A thousand AI agents run a marathon, and almost none of them ever call the LLM. Check out the full tutorial on our channel and learn:
- When to use an LLM vs deterministic logic
- The before_model_callback trick, keep the agent, skip the model
- Why route planning is deterministic (NP-hard + the Spine & Sprout algorithm)
- How 1,000 autopilot runners make 0 LLM calls
- Where the tokens actually go (the AI decides, the code runs)
- Scaling 1,000 stateless sessions with Redis

Speakers: Casey West, Annie Wang
Products Mentioned: Google Agent Development Kit, Gemini API, Agent Runtime, Google Cloud Pub/Sub, AlloyDB, Agent2Agent Protocol

$Make your website agent ready with WebMCP Join Gemini Enterprise Agent Ready (GEAR) for the latest agent resources here. → https://goo.gle/3Srblgl Access the source code on GitHub → https://goo.gle/4g8TC5t AI agents are becoming major consumers of the web, but todays websites are built for humans, not machines. WebMCP changes that. In this video, Hugo Zanini, a Google Developer Expert in AI, shows how to expose typed tools directly inside web pages so AI agents can act reliably, quickly, and at a fraction of the token cost. Watch along and learn: * The agent shopping problem and why current approaches like screenshot-scraping fall short. * How WebMCP works to expose contextual tools directly through simple browser APIs. * A live demo of Happy Coffee (a React-based developer portal) showing Gemini in action searching catalogs and analyzing quality checks. * WebMCP for Local Agents, demonstrating how local workflows can leverage web UIs to accelerate development. Watch more How I built my agent → https://www.youtube.com/playlist?list=PLIivdWyY5sqLseBL8iUmVbdd_h30C4oEo 🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech Speaker: Hugo Zanini Products Mentioned: WebMCP, React, Gemini$

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