Uploaded June 2026 | Updated September 2026, 3 weeks ago
Explore the next evolution of Google Cloud’s agent platform, designed to empower developers and enterprises to build, scale, and govern sophisticated AI agents. Discover our new, unified Gemini Enterprise Agent Platform Agent Builder, featuring a streamlined UX and a no-code/low-code Agent Designer to simplify the creation of complex, multi-agent workflows. Find out how our new Agent Registry, coupled with the agent marketplace, enables organizations to securely curate and distribute reusable components. Learn how we’re delivering enterprise-grade governance and quality at scale with a unified governance layer, new agent management capabilities, advanced observability with time-travel debugging, an integrated evaluation suite, and a managed simulation platform to ensure your agents are secure, reliable, and continuously improving. From a richer Agent Development Kit (ADK) with expanded language support to native agent-to-agent communication and a secure code execution sandbox, Google Cloud provides a comprehensive ecosystem for the entire agent life cycle.
Watch more Google Cloud Next 2026 Sessions → https://goo.gle/gcloud-next-2026
Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
Speakers: Aman Khan, Lavi Nigam, Gauthier Debuiche, Thomas Menard
BRK2-093
#GoogleCloudNext
Explore the next evolution of Google Cloud’s agent platform, designed to empower developers and enterprises to build, scale, and govern sophisticated AI agents. Discover our new, unified Gemini Enterprise Agent Platform Agent Builder, featuring a streamlined UX and a no-code/low-code Agent Designer to simplify the creation of complex, multi-agent workflows. Find out how our new Agent Registry, coupled with the agent marketplace, enables organizations to securely curate and distribute reusable components. Learn how we’re delivering enterprise-grade governance and quality at scale with a unified governance layer, new agent management capabilities, advanced observability with time-travel debugging, an integrated evaluation suite, and a managed simulation platform to ensure your agents are secure, reliable, and continuously improving. From a richer Agent Development Kit (ADK) with expanded language support to native agent-to-agent communication and a secure code execution sandbox, Google Cloud provides a comprehensive ecosystem for the entire agent life cycle.
Watch more Google Cloud Next 2026 Sessions → https://goo.gle/gcloud-next-2026
Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
Speakers: Aman Khan, Lavi Nigam, Gauthier Debuiche, Thomas Menard
BRK2-093
#GoogleCloudNext
![How to design a multi-agent system that skips the LLM
Github repo → https://goo.gle/race-condition
Previous episode → https://goo.gle/marathonagent
A thousand AI agents run a marathon, and almost none of them ever call the LLM.
In this multi-agent system deep dive, Casey West breaks down the one architectural decision behind Race Condition: a 1000-agent system built on Googles Agent Development Kit (ADK).
The question every AI engineer is wrestling with: when do you let an LLM decide, and when do you just write the code in a multiagent system? We trace one decision end to end, planning a marathon route, then show how the same idea (skip the LLM where you dont need it) scales to a thousand agents running on deterministic code.
What youll 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
Chapters
00:00 - Intro: 1,000 AI agents that dont call the LLM
00:41 - When should an agent use an LLM?
01:02 - [Demo] Planning a marathon route
01:59 - Why Google Maps cant route a marathon
05:08 - Why the LLM Is the wrong tool (NP-hard)
05:40 - The deterministic spine & sprout algorithm
06:58 - Using AI Studio to choose the algorithm
09:00 - The trick: Skip the LLM with a callback
12:26 - before_model_callback — the reveal
17:50 - Autopilot runners: 1,000 agents, 0 LLM calls
21:31 - How many tokens? Where they actually go
23:28 - The second cost: Session state & redis
29:05 - Wrap up
More resources:
Google Agent Development Kit (ADK) → https://goo.gle/3PItVzL
Google ADK Community (Redis session service) → https://goo.gle/4ugzmUw
Agent Runtime → https://goo.gle/4nXDhnX
Google Cloud Memory Store → https://goo.gle/4nXxBtT
Agent2Agent Protocol (A2A) protocol → https://goo.gle/4u5x8HF
Casey West on LinkedIn → https://goo.gle/4dXnsJr
Annie Wang on LinkedIn → https://goo.gle/43GCXAo
Watch more Hands on AI → https://www.youtube.com/playlist?list=PLIivdWyY5sqKnJOvP89yF8t9mWuzMTcbM
🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
#AIAgent #GoogleADK #Gemini #MultiAgentSystem #AgenticAI #GoogleCloud
Speakers: Casey West, Annie Wang
Products Mentioned: Google Agent Development Kit, Gemini API, Agent Runtime, Google Cloud Pub/Sub, AlloyDB, Agent2Agent Protocol How to design a multi-agent system that skips the LLM](https://i.ytimg.com/vi/Fzd0BWMH65s/mqdefault.jpg)









