🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
GCPSPARKSAGENTICINFRA

Agentic Infrastructure for the Autonomous Enterprise on Google Cloud Training

This course provides a technical guide to enable Solution Architects to shift from building isolated chatbots to deploying persistent, Gemini Enterprise-enabled AI workers on Google Cloud. Participants will master agentic memory design, API-driven tool orchestration, and infrastructure governance using the Google Cloud Agent Platform—including the Vertex AI Reasoning Engine for persistent state management and Agent Extensions for departmental integration. Learners will move beyond "Instructional Hope" to technical enforcement, building the "Paved Road" required to orchestrate multi-agent fleets and secure non-human identities.

Google Cloud
✓ Official training Google CloudLevel Intermediate⏱️ 0.5 day (3h)

What you will learn

  • Diagnose Agency Gaps: Strip out "Track A" conversational habits and map structural frictions.
  • Architect the Engine: Deploy persistent, context-aware reasoning loops inside the Build Tier.
  • Secure the Perimeter: Enforce Zero Trust patterns and runtime protections via the Govern Tier.
  • Scale the Fleet: Mitigate reasoning drift and measure ROI using the Optimize & Scale Tiers.

Prerequisites

  • Completion of Organizing for AI Success.
  • Foundational knowledge of Google Cloud (VPC, IAM, Cloud Run).
  • Helpful to be familiar with: Python basics, REST API structures, RAG concepts.

Target audience

  • Enterprise Architects: Who need to build a scalable, secure backbone for persistent, Gemini Enterprise-enabled AI workers., Systems Integrators: Tasked with connecting AI to legacy ERP/CRM data via secure, cross-departmental tool orchestration using the Google Cloud Agent Platform., IT Directors: Who need to move from "Project Thinking" to "Platform Thinking" to manage a global fleet of autonomous agents while maintaining governance and scalability.

Training Program

5 modules to master the fundamentals

Objectives
  • Analyze the "Agency Gap" by diagnosing the functional intersection of Reasoning, Memory, and Tools with Data Governance to move from Track A (Chat) to Track B (Workers).
  • Diagnose the four technical frictions (Integration, Statelessness, Latency, and Governance) that prevent AI pilots from scaling into production.
  • Evaluate infrastructure readiness using the L1–L5 Maturity Scale to prioritize "Paved Road" investments for Level 4+ autonomy.
Topics covered
  • →The Pillars & Governance
  • →The Architectural Friction Forces
  • →The Autonomous Maturity Scale
Activities

1 Use Case, 2 Case Studies, 1 Demo

Objectives
  • Apply the Vertex AI SDK and Vertex AI Reasoning Engine to standardize agent deployment and manage persistent conversation state.
  • Evaluate the trade-offs between AlloyDB and Vertex AI Vector Search to select the optimal storage layer for metadata-heavy vs. high-scale agents.
  • Apply specific orchestration patterns (Hub-and-Spoke, Linear Relay, or Parallel Critic) to manage complex, multi-departmental goals.
  • Design an agentic deployment arc from Sandbox to Certified production to ensure infrastructure precedes autonomous action.
Topics covered
  • →Reference Stack & Tool Archetypes
  • →The Memory Decision Guide
  • →Multi-Agent Orchestration Patterns
  • →The Paved Road Lifecycle
Activities

4 Demos

Objectives
  • Apply agentic threat modeling to identify and mitigate risks like Indirect Prompt Injection and Tool-Chaining exploits.
  • Apply a three-layer identity model using Workload Identity Federation to ensure "Least Privilege" for autonomous workers.
  • Apply Model Armor as a real-time security proxy to filter malicious inputs and redact sensitive output data.
  • Analyze Responsible AI production requirements to embed accountability, traceability, and "Human-in-the-Loop" checkpoints within the Autonomous Perimeter.
Topics covered
  • →Threat Modeling for Agentic Systems
  • →Identity Hierarchy & Credentials
  • →Defending the Boundary: Model Armor
  • →Responsible AI & Human-in-the-Loop
Activities

1 Use Case, 4 Demos

Objectives
  • Analyze platform ROI by shifting from vanity metrics to Infrastructure Leverage Ratios and Component Reusability to prove the value of the "Paved Road."
  • Apply a continuous feedback loop using Golden Datasets and reasoning traces to detect and remediate "Reasoning Drift."
  • Apply the "Innovation Harvest" methodology to scale successful siloed tools into global, certified Gemini Enterprise assets.
Topics covered
  • →Infrastructure ROI
  • →The GenAIOps Lifecycle
  • →The Innovation Harvest
Activities

1 Use Case, 1 Demo

Objectives
  • Evaluate understanding of core course concepts through scenario-based questions.
Topics covered
  • →Review of Core Concepts
Activities

5 scenario-based multiple choice questions

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Quality Process

SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach

Teaching Methods Used
  • Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
  • Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
  • Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System

The achievement of training objectives is evaluated at multiple levels to ensure quality:

  • Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
  • Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
  • Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.

Frequently Asked Questions

This intermediate course is designed for solution architects who want to move beyond isolated chatbots and deploy persistent, Gemini Enterprise-enabled AI workers on Google Cloud.
As a level 200 course, it assumes familiarity with Google Cloud services and core architecture concepts. Prior experience with conversational AI is helpful but not mandatory.
You will master agentic memory design, API-driven tool orchestration, and infrastructure governance, deploying the Vertex AI Reasoning Engine and securing multi-agent fleets on Google Cloud.
In the Building the Paved Road module, you deploy persistent, context-aware reasoning loops inside the Build Tier using the Vertex AI Reasoning Engine, giving agents reliable state management.
The course lasts 3 hours and is delivered in an instructor-led format by SFEIR Institute.
Yes. This is an official Google Cloud Sparks course delivered by SFEIR Institute, a certified Google Cloud Training Partner.

395€ excl. VAT

per learner

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