Master the AI Coding Tool Built for Enterprise
Open-source, governed, and Google Cloud native — learn Gemini CLI the enterprise way
Taught by Dan King, 4x Google Trainer of the Year
Public class policy: Classes run with a minimum of 6 participants. If minimum enrollment isn't reached, you'll be notified 7 days before with options to transfer or receive a full refund.
Enterprise-First Approach
Start with the governance story. Learn Policy Engine, Trusted Folders, and compliance hooks before writing a single line of AI-generated code — so you can champion adoption with confidence.
Beyond Code Completion
Gemini CLI is a full agentic coding tool with sub-agents, multimodal input, and MCP integrations. This is not autocomplete — it is an AI teammate that understands your codebase.
Free and Open Source
No subscription required. Gemini CLI is Apache 2.0 with a generous free tier. Enterprise teams upgrade to Vertex AI for governance, not to unlock features.
Hands-On Every Module
6 labs across the day. Configure security, scaffold a microservice, build an MCP server, and deploy to Cloud Run. You leave with real skills, not slide decks.
Overview
Learn to adopt Gemini CLI across your engineering team with enterprise-grade security, governance, and Google Cloud integration. This hands-on course takes an enterprise-first approach — starting with the Policy Engine, Trusted Folders, and compliance hooks before diving into agentic coding with sub-agents, multimodal context, MCP integrations, and Cloud Run deployment.
What You'll Learn
- Evaluate Gemini CLI against other AI coding tools for enterprise use
- Configure enterprise security controls using Policy Engine, Trusted Folders, and compliance hooks
- Use agentic coding with sub-agents and approval gates for safe code generation
- Create hierarchical GEMINI.md context with multimodal input for team-wide standards
- Build custom MCP servers and discover Agent Skills for extending capabilities
- Deploy applications to Google Cloud Run and integrate with GCP services
- Design an AI coding tool adoption strategy with measurable success criteria
Who Should Attend
Enterprise developers evaluating or adopting AI coding tools
Products Covered
Course Modules
The Enterprise Case for AI Coding Tools
Topics
- AI coding tools landscape — Gemini CLI vs Claude Code vs GitHub Copilot CLI vs Cursor
- Open-source advantage — Apache 2.0, inspectable code, no vendor lock-in
- Cost model — free tier vs Vertex AI for governed usage
- Security architecture — sandboxing, Policy Engine, Trusted Folders, .geminiignore
- Data flow and privacy — /privacy command, telemetry controls
- Enterprise readiness checklist
Learning Outcomes
- Compare AI coding tools across security, cost, and capability dimensions
- Articulate Gemini CLI's enterprise value proposition to stakeholders
Activities
Governance & Security Controls
Topics
- Policy Engine — allow/deny/ask_user actions with regex patterns, priority-based rule resolution
- Trusted Folders — restricting capabilities in untrusted directories, /permissions command
- Hooks for compliance — BeforeTool for policy enforcement, AfterTool for audit logging
- .geminiignore — excluding sensitive files from context
- Network controls — proxy routing, egress restrictions
- Vertex AI backend — governed model access with audit trails, VPC-SC compatibility
- Team configuration — version-controlled settings.json, wrapper scripts
Learning Outcomes
- Configure Policy Engine rules with deny-by-default posture
- Set up compliance hooks for audit logging
- Manage Trusted Folders and file exclusions
Activities
Core Capabilities & Agentic Coding
Topics
- Installation and authentication — API key vs Vertex AI, Cloud Shell quick-start, /init
- The ReAct loop — reason, plan, act with approval gates
- Code generation — single-file and multi-file from prompts
- Refactoring — architectural changes across a codebase
- Built-in tools — run_shell_command, Google Search grounding, PTY support, file system
- Sub-agents — codebase_investigator for mapping architectural dependencies
- Plan review — reviewing and editing agent plans before execution
Learning Outcomes
- Use agentic coding patterns with approval gates for safe code generation
- Leverage codebase_investigator for enterprise-scale refactoring
Activities
Context Management & Multimodal Input
Topics
- Hierarchical GEMINI.md — home to project to module to component layering, /init for auto-generation
- Team standards as context — coding conventions, framework patterns, security policies
- File imports — @file.md syntax for modular context composition
- Multimodal input — @diagram.png for architecture diagrams, @spec.pdf for design specs, audio/video
- Memory system — /memory show, /memory add, /memory refresh, save_memory tool
- Conductor extension — official extension for Context-Driven Development
Learning Outcomes
- Design hierarchical context files for team-wide coding standards
- Use multimodal input to provide visual and document context
Activities
MCP & Skills — Extending Gemini CLI
Topics
- Model Context Protocol — tools, resources, prompts
- Official Google MCP servers — Cloud Run, Google Cloud Services, Workspace
- Building custom MCP servers — FastMCP for Python, STDIO transport
- Authentication patterns — API keys, OAuth, service account impersonation
- Enterprise MCP governance — approval and audit strategy
- Agent Skills — /skills for discovery, activate_skill for specialized personas
Learning Outcomes
- Build and integrate custom MCP servers for internal systems
- Discover and activate Agent Skills for specialized tasks
Activities
Google Cloud Integration Deep Dive
Topics
- Cloud Shell integration — zero-install, pre-authenticated
- Cloud Run deployment — /deploy for one-line deployments, Artifact Registry
- /setup-github — GitHub Actions for automated issue triage and PR reviews
- Vertex AI integration — GOOGLE_GENAI_USE_VERTEXAI=true, service account auth, VPC-SC
- GCP MCP servers — Cloud Run MCP, Google Cloud Services MCP
- Cost tracking — /stats for token usage, Vertex AI billing dashboard
Learning Outcomes
- Deploy applications to Cloud Run using Gemini CLI
- Integrate with GCP services and monitor usage costs
Activities
Enterprise Adoption Playbook
Topics
- Adoption strategy — pilot, evaluate, expand pattern
- Measuring impact — /stats for token usage, productivity metrics, developer satisfaction
- Common objections — security, cost, accuracy — and how to address them
- Template artifacts — GEMINI.md templates, Policy Engine configs, hook library, MCP starters
- Ongoing learning — community resources, release cadence, Conductor for team knowledge
Learning Outcomes
- Design an AI coding tool adoption strategy with measurable success criteria
- Address common enterprise objections to AI coding tools
Activities
Get This Training
No public classes currently scheduled. Express interest below or request private training.
Course Details
- Course Code
- AI-105
- Duration
- 1 day
- Format
- instructor-led
- Level
- Intermediate
- Hands-on Labs
- 6
- Modules
- 7
What Makes This Workshop Different
Policy Engine & Security
Configure allow/deny/ask_user rules with regex patterns. Set up Trusted Folders, .geminiignore, and compliance hooks for enterprise-grade control.
Agentic Coding with Sub-Agents
Use the ReAct loop with approval gates. Leverage codebase_investigator for architectural analysis across large enterprise codebases.
Multimodal Context
Feed architecture diagrams, PDF specs, and audio into Gemini CLI as context. Generate code from visual designs and written specifications.
Custom MCP Servers
Build FastMCP servers that connect Gemini CLI to your internal APIs. Query engineering metrics, internal documentation, and team data through natural language.
Google Cloud Integration
Deploy to Cloud Run with a single command. Set up Vertex AI backend, GitHub Actions integration, and cost monitoring.
Adoption Playbook
Walk away with templates, Policy Engine configs, hook libraries, and a structured pilot-evaluate-expand strategy for your organisation.
“I needed to convince our security team that AI coding tools could be used safely. This workshop gave me the technical proof — Policy Engine configs, audit hooks, Trusted Folders. I walked in sceptical and left with a deployment plan.”
James Rivera
Senior Developer, Financial Services Firm
Frequently Asked Questions
What do I need to know before attending?
Is this just a Gemini CLI tutorial?
How does Gemini CLI compare to Claude Code or GitHub Copilot?
Do I need my own GCP project?
What if my company has strict security requirements?
Do I get a certificate?
Give Your Team AI Coding Tools They Can Actually Use at Work
Enterprise-governed, open-source, and Google Cloud native — adopt Gemini CLI with security built in from day one