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Duration 14 hours
Course Outline
Exploring Google Antigravity's Architecture
- Agent-first design principles
- The roles of Editor and Manager interfaces
- Workspace structure and execution contexts
Configuring Agents and Capabilities
- Assigning specific roles and specializations to agents
- Defining task boundaries and levels of autonomy
- Managing security protocols and permissions for agents
Designing Multi-Agent Workflows
- Planning and sequencing workflow steps
- Coordinating background and foreground agents
- Utilizing chaining, delegation, and escalation patterns
Navigating 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 the accuracy of agent output
- 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
- A solid understanding of modern DevOps and platform engineering concepts
- Practical experience with AI-assisted development workflows
- Familiarity with distributed systems or cloud environments
Target Audience
- Platform engineers
- DevOps engineers
- AI architects