Operationalizing AI Agents on Google Cloud (OAAGC) – Outline

Detailed Course Outline

Module 1 - Agentic AI Imperative, Taxonomy and the Operational Cycle

Topics:

  • The agentic imperative
  • AI agent taxonomy
  • Agentic scaling challenges
  • Identifying agentic use cases
  • Introducing GCP products for operationalizing agents

Objectives:

  • Explain the components and core definitions of AI agents
  • Evaluate organizational readiness using the AI autonomy maturity model
  • Select the appropriate Google Cloud platform for agent deployment based on architecture requirements.

Activities:

  • 1x reflection

Module 2 - Design Patterns and Grounded Reasoning

Topics:

  • Multi-agent design patterns
  • Interoperability standards
  • Grounding and memory
  • Agentic AI operational cycle

Objectives:

  • Design multi-agent architectures using industry design patterns
  • Prevent infinite loops during reasoning execution
  • Integrate models with external tools via standard protocols
  • Build robust long-term memory structures for enterprise data grounding

Activities:

  • 1x reflection

Module 3 - Governance, Security, and Runtimes

Topics:

  • Agentic AI governance gap
  • Agentic AI governance strategy
  • Security architecture on GCP
  • The human-agent control mandate
  • AI CoE and AI review committees

Objectives:

  • Implement policy-as-code and tool governance across agent fleets
  • Select platform-level security barriers to prevent data exfiltration or malicious injection
  • Construct clear escalation matrices defining boundaries for autonomous actions vs. human verification

Activities:

  • 1x discussion

Module 4 - Performance, ROI, and the Path to Production

Topics:

  • Agent evaluation
  • FinOps for Agentic AI
  • Practical implementation roadmap
  • 5 phases of the production-ready operational cycle
  • Industry example: Marketing

Objectives:

  • Formulate an evaluation strategy to measure reasoning coherence and tool use success
  • Execute production-scale agent monitoring using pre-defined trajectory metrics
  • Establish cost-control optimization guardrails to maximize token efficiency and business value delivery

Module 5 - Summary and course quiz

Topics:

  • Course recap
  • Q&A session
  • MCQ quiz

Objectives:

  • Reinforce topics learned during the Spark

Activities:

  • 1x quiz (4 MCQs)