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Duration 14 hours
Course Outline
Comprehending the Architecture of Google Antigravity
- Agent-first design principles
- The roles of the Editor and Manager interfaces
- Workspace structure and execution contexts
Configuring Agents and Their Capabilities
- Assigning specific roles and specializations to agents
- Defining task boundaries and levels of autonomy
- Administering security and permissions for agents
Designing Multi-Agent Workflows
- Planning and sequencing workflow steps
- Coordinating background and foreground agents
- Applying chaining, delegation, and escalation patterns
Utilizing the Manager (Mission-Control) Interface
- Monitoring real-time agent activity
- Analyzing graphs, states, and execution timelines
- Intervening, overriding, or redirecting agent tasks
Creating and Managing Antigravity Artifacts
- Task lists, work plans, and decision traces
- Screenshots, browser recordings, and workspace captures
- Audit logs and reproducibility metadata
Techniques for Verification and Quality Assurance
- Ensuring traceability and transparency
- Validating the accuracy of agent outputs
- Implementing safeguards and failover strategies
Integrating Antigravity into Engineering Pipelines
- Supporting CI/CD and release workflows
- Collaborating with established DevOps tools
- Scaling agent tasks across teams and environments
Advanced Optimization for Multi-Agent Collaboration
- Minimizing redundant actions and cycles
- Leveraging performance metrics and analytics
- Designing resilient and adaptable workflows
Summary and Next Steps
Requirements
- A solid grasp of modern DevOps and platform engineering concepts
- Practical experience with AI-assisted development workflows
- Knowledge of distributed systems or cloud environments
Audience
- Platform engineers
- DevOps engineers
- AI architects