Agentic Infrastructure for the Autonomous Enterprise on Google Cloud
This 2-3 hour Cloud Sparks session is a short, interactive look at the infrastructure behind autonomous AI agents on Google Cloud.
In Agentic Infrastructure for the Autonomous Enterprise on Google Cloud, solution architects learn how to move from isolated chatbots to persistent, Gemini Enterprise-enabled AI workers. The session starts by diagnosing the gap between conversational AI and autonomous workers, including the integration, statelessness, latency and governance frictions that keep AI pilots from reaching production. From there it covers a reference stack for the Google Cloud Agent Platform, a decision guide for agent memory (including a comparison of AlloyDB and Agent Search), multi-agent orchestration patterns such as hub-and-spoke, linear relay and parallel critic, and a deployment path from sandbox to certified production.
The second half focuses on keeping autonomous agents safe and sustainable. Participants look at threat modeling for agentic systems, a three-layer identity model with least privilege, Model Armor as a real-time filter for inputs and outputs, and human-in-the-loop checkpoints. They finish with measuring platform ROI, spotting and fixing reasoning drift, and scaling successful tools into shared enterprise assets. Along the way you work through use cases, case studies and demos, and close with a short scenario-based quiz.
The session is aimed at enterprise architects, systems integrators and IT directors. Participants should have completed Organizing for AI Success and have foundational Google Cloud knowledge (VPC, IAM, Cloud Run). Basic Python, REST APIs and RAG concepts are helpful. This is an architecture session, so prompt engineering basics, model fine-tuning and standard chatbot UIs are not covered.