Course Overview
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.
Who should attend
- 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.
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.
Course Objectives
- 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).
Outline: Agent Observability on Google Cloud (AOGC)
Module 1 - The Google Cloud Observability Foundation
Topics:
- The Agent Observability Mandate
- Tracing the Agent Engine Workflow
- Establishing the Immutable Audit Trail
Objectives:
- Explain Non-Deterministic behavior
- Deconstruct runs into Cloud Trace Spans
- Justify Immutable Audit Trail for trust
Activities:
- 4 demos
Module 2 - Proactive Monitoring and Evaluation
Topics:
- Implementing Real-Time Metrics
- Designing Actionable Alerting Policies
- Evaluation for Continuous Improvement
Objectives:
- Create custom dashboards for Cost & Performance
- Design Actionable Alerts to prevent budget overruns
- Establish a continuous quality loop with Golden Test cases
Activities:
- 4 demos
Module 3 - Tools for Observability on Google Cloud
Topics:
- Observability for Audit and Security
- Scaling Agent Development and Deployment
- Scaling the Observable Enterprise
Objectives:
- Implement Governance Controls for PII compliance
- Evaluate deployment trade-offs for Scaling
- Align technical metrics with Business KPIs
Activities:
- 2 demos
Module 4 - Agent Observability on Google Cloud: Quiz/Reflection
Topics:
- Review of Core Concepts
Objectives:
- Evaluate understanding of core courses concepts through scenario-based questions
Activities:
- 5 scenario-based multiple choice questions