🏆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
GCPSPARKSAGENTOBS

Agent Observability on Google Cloud Training

This course provides an applied, intermediate guide to operationalizing AI agents, focusing specifically on achieving production confidence and cost predictability for Gemini-powered workflows on Google Cloud. Participants will learn the methodology and actionable skills necessary to transform non-deterministic agent logic into transparent, auditable, and scalable systems. The course covers core operational disciplines, including mapping the agent's complex thought process (ReAct loops) to Cloud Trace Spans for debugging, implementing Logs-Based Security Metrics for compliance, and setting up actionable alerts and custom dashboards in Cloud Monitoring to proactively control cost overruns and quality drift. The course uses presentations, Visual Walkthroughs, and strategic discussions to ensure effective learning that is directly applicable to the Vertex AI ecosystem.

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

What you will learn

  • Trace non-deterministic agent logic using Cloud Trace Spans and the ReAct loop.
  • Implement cost and quality controls using custom Cloud Monitoring dashboards.
  • Establish a continuous quality loop with Golden Test Cases.
  • Implement governance and auditability using Logs-Based Security Metrics.
  • Align technical observability metrics with Business KPIs (Cost, ROI).

Prerequisites

  • Foundational Knowledge (Mandatory): Familiarity with foundational Machine Learning (ML) concepts, specifically the distinction between models and agents.
  • Experience with Google Cloud concepts and services, including basic navigation of the Google Cloud console.
  • Familiarity with software development principles and development lifecycles (DevOps/MLOps).
  • Highly Beneficial (Recommended): Experience with the Google Cloud CLI and Vertex AI services.
  • Basic understanding of Git/version control knowledge as it relates to deploying code.
  • Familiarity with structuring logs (e.g., JSON) and setting up basic monitoring alerts.

Target audience

  • AI/ML Engineer: Needs to understand how trace data (ReAct Spans) helps debug non-deterministic reasoning and how to measure quality metrics (Hallucination Rate) for strategic decisions., Data Scientist: Needs visibility into performance trends, evaluation results (Golden Test Cases), and compliance issues to ensure the agent's ethical behavior and data integrity., SRE/DevOps Engineer: Responsible for operationalizing the agent. Needs to know how to adapt monitoring for cost spikes, implement P99 latency alerts, and manage deployment trade-offs (Agent Engine vs. Cloud Run)., The course is also intended for intermediate technical staff, technical leads, and MLOps Engineers, or anyone involved in designing, implementing, or managing the observability, governance, or production scaling of Gemini-powered agentic workflows on Google Cloud.

Training Program

4 modules to master the fundamentals

Objectives
  • Explain Non-Deterministic behavior.
  • Deconstruct runs into Cloud Trace Spans.
  • Justify Immutable Audit Trail for trust.
Topics covered
  • →The Agent Observability Mandate
  • →Tracing the Agent Engine Workflow
  • →Establishing the Immutable Audit Trail
Activities

4 demos

Objectives
  • Create custom dashboards for Cost & Performance.
  • Design Actionable Alerts to prevent budget overruns.
  • Establish a continuous quality loop with Golden Test Cases.
Topics covered
  • →Implementing Real-Time Metrics
  • →Designing Actionable Alerting Policies
  • →Evaluation for Continuous Improvement
Activities

4 demos

Objectives
  • Implement Governance Controls for PII compliance.
  • Evaluate deployment trade-offs for Scaling.
  • Align technical metrics with Business KPIs.
Topics covered
  • →Observability for Audit and Security
  • →Scaling Agent Development and Deployment
  • →Scaling the Observable Enterprise
Activities

2 demos

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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Upcoming sessions

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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 applied, intermediate course is designed for developers, SREs, and DevOps engineers who need to operationalize Gemini-powered AI agents with production confidence and cost predictability.
As a level 200 course, it assumes comfort with Google Cloud operations. Some prior experience with Gemini or AI agents helps but is not strictly required.
You will learn to turn non-deterministic agent logic into transparent, auditable systems: tracing with Cloud Trace, monitoring cost and quality, running Golden Test Cases, and applying Logs-Based Security Metrics.
You map the agent's ReAct reasoning loop to Cloud Trace Spans, making its complex thought process visible so you can debug non-deterministic behavior step by step.
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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