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Course Outline
Comprehending the Architecture of Google Antigravity
- Agent-first design principles
- The specific roles of Editor and Manager interfaces
- Workspace organization and execution contexts
Agent and Capability Configuration
- Assigning roles and areas of specialization to agents
- Establishing task boundaries and levels of autonomy
- Governing agent security and permissions
Designing Multi-Agent Workflows
- Planning and sequencing workflow steps
- Coordinating between background and foreground agents
- Applying chaining, delegation, and escalation patterns
Utilizing the Manager (Mission-Control) Interface
- Tracking live agent activities
- Interpreting graphs, states, and execution timelines
- Intervening to override or redirect agent tasks
Creation and Management of Antigravity Artifacts
- Task lists, work plans, and decision traces
- Screenshots, browser recordings, and workspace captures
- Audit logs and reproducibility metadata
Verification and Quality Assurance Methods
- Maintaining traceability and transparency
- Validating the accuracy of agent outputs
- Implementing safeguards and failover strategies
Integrating Antigravity into Engineering Pipelines
- Supporting CI/CD and release processes
- Collaborating with established DevOps tools
- Scaling agent tasks across various 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 contemporary DevOps and platform engineering concepts
- Hands-on experience with AI-assisted development workflows
- Working knowledge of distributed systems or cloud environments
Target Audience
- Platform engineers
- DevOps engineers
- AI architects
14 Hours