Course Overview
This course equips technical professionals and cloud architects with the specialized skills needed to deploy, manage, and optimize autonomous AI agents at scale within an enterprise environment. It covers the core taxonomy and operational lifecycle of agentic AI, multi-agent design patterns, enterprise governance and security frameworks, and advanced full-stack evaluation metrics.
Participants will learn how to leverage Google Cloud services, such as the Gemini Enterprise Agent Platform, Google Kubernetes Engine (GKE) Autopilot, and Spanner Graph, to construct secure, scalable, and cost-effective autonomous multi-agent systems that drive business value while mitigating operational, identity, and economic risks.
Who should attend
Solutions architects, lead AI engineers, and technology leaders with technical knowledge of cloud infrastructure who plan to build, scale, and govern autonomous multi-agent fleets within an enterprise setting.
Prerequisites
Familiarity with Google Cloud core services, AI foundational concepts, and standard model application programming interfaces (APIs).
Course Objectives
- Analyze an organization's operational landscape and determine its current position on the 5-level AI autonomy maturity model.
- Design scalable multi-agent architectures using design patterns.
- Apply the 12 non-negotiable governance capabilities for autonomous systems to mitigate autonomous risk.
- Evaluate agent performance using multi-step trajectory metrics and formulate a FinOps optimization strategy
Outline: Operationalizing AI Agents on Google Cloud (OAAGC)
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)