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Course Outline
Comprehending Google Antigravity's Architecture
- Principles of agent-first design
- Functions of the Editor and Manager interfaces
- Workspace structure and execution contexts
Configuring Agents and Capabilities
- Assigning specialized roles to agents
- Defining task boundaries and levels of autonomy
- Managing security protocols and permissions for agents
Designing Multi-Agent Workflows
- Planning and sequencing workflows
- Coordinating between background and foreground agents
- Applying chaining, delegation, and escalation patterns
Utilizing the Manager (Mission-Control) Interface
- Monitoring real-time agent activity
- Interpreting graphs, states, and execution timelines
- Intervening, overriding, or redirecting agent tasks as needed
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 outputs
- Implementing safeguards 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
- Knowledge of modern DevOps and platform engineering principles
- Experience with AI-assisted development workflows
- Familiarity with distributed systems or cloud environments
Target Audience
- Platform engineers
- DevOps engineers
- AI architects
14 Hours
