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Course Outline
Understanding Google Antigravity's Architecture
- Agent-first design principles
- Roles of the Editor and Manager interfaces
- Workspace structure and execution contexts
Configuring Agents and Capabilities
- Assigning agent roles and specializations
- Defining task boundaries and autonomy levels
- Managing security and permissions for agents
Designing Multi-Agent Workflows
- Workflow planning and sequencing
- Coordinating background and foreground agents
- Using chaining, delegation, and escalation patterns
Working with the Manager (Mission-Control) Interface
- Monitoring live agent activity
- Interpreting graphs, states, and execution timelines
- Intervening, overriding, or redirecting agent tasks
Generating and Managing Antigravity Artifacts
- Task lists, work plans, and decision traces
- Screenshots, browser recordings, and workspace captures
- Audit logs and reproducibility metadata
Verification and Quality Assurance Techniques
- Ensuring traceability and transparency
- Validating agent output accuracy
- Implementing safe-guards and failover strategies
Integrating Antigravity into Engineering Pipelines
- Supporting CI/CD and release workflows
- Collaborating with existing DevOps tools
- Scaling agent tasks across teams and environments
Advanced Optimization for Multi-Agent Collaboration
- Reducing redundant actions and cycles
- Leveraging performance metrics and analytics
- Designing resilient and adaptable workflows
Summary and Next Steps
Requirements
- An understanding of modern DevOps and platform engineering concepts
- Experience with AI-assisted development workflows
- Familiarity with distributed systems or cloud environments
Audience
- Platform engineers
- DevOps engineers
- AI architects
14 Hours
