Agentic Infrastructure for the Autonomous Enterprise on Google Cloud

Agentic Infrastructure for the Autonomous Enterprise on Google CloudAIAEGCGOGoogleGO-AIAEGC1.0<ul> <li>Diagnose Agency Gaps: Strip out "Track A" conversational habits and map structural frictions.</li><li>Architect the Engine: Deploy persistent, context-aware reasoning loops inside the Build Tier.</li><li>Secure the Perimeter: Enforce Zero Trust patterns and runtime protections via the Govern Tier.</li><li>Scale the Fleet: Mitigate reasoning drift and measure ROI using the Optimize & Scale Tiers.</li></ul><ul> <li>Completion of Organizing for AI Success.</li><li>Foundational knowledge of Google Cloud (VPC, IAM, Cloud Run).</li></ul>Helpful to be familiar with: <ul> <li>Python basics</li><li>REST API structures</li><li>RAG concepts</li></ul><ul> <li>Enterprise Architects: Who need to build a scalable, secure backbone for persistent, Gemini Enterprise-enabled AI workers.</li><li>Systems Integrators: Tasked with connecting AI to legacy ERP/CRM data via secure, cross-departmental tool orchestration using the Google Cloud Agent Platform.</li><li>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</li></ul><h4>Module 1 - The Shift to Agency</h4> Topics: <ul> <li>1. The Pillars & Governance</li><li>2. The Architectural Friction Forces</li><li>3. The Autonomous Maturity Scale</li></ul>Objectives: <ul> <li>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).</li><li>Diagnose the four technical frictions (Integration, Statelessness, Latency, and Governance) that prevent AI pilots from scaling into production.</li><li>Evaluate infrastructure readiness using the L1–L5 Maturity Scale to prioritize "Paved Road" investments for Level 4+ autonomy.</li></ul>Activities: <ul> <li>1 Use Case</li><li>2 Case Studies</li><li>1 Demo</li></ul><h4>Module 2 - Building the "Paved Road"</h4> Topics: <ul> <li>1. Reference Stack & Tool Archetypes</li><li>2. The Memory Decision Guide</li><li>3. Multi-Agent Orchestration Patterns</li><li>4. The Paved Road Lifecycle</li></ul>Objectives: <ul> <li>Apply the Vertex AI SDK and Vertex AI Reasoning Engine to standardize agent deployment and manage persistent conversation state.</li><li>Evaluate the trade-offs between AlloyDB and Vertex AI Vector Search to select the optimal storage layer for metadata-heavy vs. high-scale agents.</li><li>Apply specific orchestration patterns (Hub-and-Spoke, Linear Relay, or Parallel Critic) to manage complex, multi-departmental goals.</li><li>Design an agentic deployment arc from Sandbox to Certified production to ensure infrastructure precedes autonomous action.</li></ul>Activities: <ul> <li>4 Demos</li></ul><h4>Module 3 - The Autonomous Perimeter</h4> Topics: <ul> <li>1. Threat Modeling for Agentic Systems</li><li>2. Identity Hierarchy & Credentials</li><li>3. Defending the Boundary: Model Armor</li><li>4. Responsible AI & Human-in-the-Loop</li></ul>Objectives: <ul> <li>Apply agentic threat modeling to identify and mitigate risks like Indirect Prompt Injection and Tool-Chaining exploits.</li><li>Apply a three-layer identity model using Workload Identity Federation to ensure "Least Privilege" for autonomous workers.</li><li>Apply Model Armor as a real-time security proxy to filter malicious inputs and redact sensitive output data.</li><li>Analyze Responsible AI production requirements to embed accountability, traceability, and "Human-in-the-Loop" checkpoints within the Autonomous Perimeter.</li></ul>Activities: <ul> <li>1 Use Case</li><li>4 Demos</li></ul><h4>Module 4 - Sustaining Autonomy</h4> Topics: <ul> <li>1. Infrastructure ROI</li><li>2. The GenAIOps Lifecycle</li><li>3. The Innovation Harvest</li></ul>Objectives: <ul> <li>Analyze platform ROI by shifting from vanity metrics to Infrastructure Leverage Ratios and Component Reusability to prove the value of the "Paved Road."</li><li>Apply a continuous feedback loop using Golden Datasets and reasoning traces to detect and remediate "Reasoning Drift."</li><li>Apply the "Innovation Harvest" methodology to scale successful siloed tools into global, certified Gemini Enterprise assets.</li></ul>Activities: <ul> <li>1 Use Case</li><li>1 Demo</li></ul><h4>Module 5 - Summary and Quiz</h4> Topics: <ul> <li>Review of Core Concepts</li></ul>Objectives: <ul> <li>Evaluate understanding of core course concepts through scenario-based questions.</li></ul>Activities: <ul> <li>5 scenario-based multiple choice questions.</li></ul>- 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.- 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- 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 scalabilityModule 1 - The Shift to Agency Topics: - 1. The Pillars & Governance - 2. The Architectural Friction Forces - 3. The Autonomous Maturity Scale 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. Activities: - 1 Use Case - 2 Case Studies - 1 Demo Module 2 - Building the "Paved Road" Topics: - 1. Reference Stack & Tool Archetypes - 2. The Memory Decision Guide - 3. Multi-Agent Orchestration Patterns - 4. The Paved Road Lifecycle 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. Activities: - 4 Demos Module 3 - The Autonomous Perimeter Topics: - 1. Threat Modeling for Agentic Systems - 2. Identity Hierarchy & Credentials - 3. Defending the Boundary: Model Armor - 4. Responsible AI & Human-in-the-Loop 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. Activities: - 1 Use Case - 4 Demos Module 4 - Sustaining Autonomy Topics: - 1. Infrastructure ROI - 2. The GenAIOps Lifecycle - 3. The Innovation Harvest 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. Activities: - 1 Use Case - 1 Demo Module 5 - Summary and Quiz Topics: - Review of Core Concepts Objectives: - Evaluate understanding of core course concepts through scenario-based questions. Activities: - 5 scenario-based multiple choice questions.3 hours350.00485.00300.00300.00300.00300.00300.00