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Duration 14 hours
Course Outline
Exploring Google Antigravity's Architecture
- Core principles of agent-first design
- Functions of the Editor and Manager interfaces
- Workspace organization and execution contexts
Agent Configuration and Capability Definition
- Allocating specific roles and specializations to agents
- Setting task boundaries and defining autonomy levels
- Overseeing security settings and agent permissions
Architecting Multi-Agent Workflows
- Strategic planning and task sequencing
- Synchronizing background and foreground agent activities
- Utilizing chaining, delegation, and escalation patterns
Utilizing the Manager (Mission-Control) Interface
- Observing real-time agent activity
- Reading graphs, states, and execution timelines
- Intervening in, overriding, or redirecting agent tasks as needed
Production and Management of Antigravity Artifacts
- Reviewing task lists, work plans, and decision traces
- Analyzing screenshots, browser recordings, and workspace captures
- Managing audit logs and reproducibility metadata
Verification and Quality Assurance Methods
- Maintaining traceability and transparency in operations
- Verifying the accuracy of agent outputs
- Establishing safeguards and failover mechanisms
Integrating Antigravity into Engineering Pipelines
- Supporting CI/CD and release management processes
- Interoperating with current DevOps toolsets
- Scaling agent tasks across various teams and environments
Advanced Optimization for Multi-Agent Collaboration
- Minimizing redundant actions and processing cycles
- Utilizing performance metrics and analytics for insights
- Crafting resilient and adaptable workflow structures
Conclusion and Future Directions
Requirements
- A solid grasp of modern DevOps and platform engineering principles
- Hands-on experience with AI-assisted development cycles
- Knowledge of distributed systems or cloud infrastructure
Target Audience
- Platform engineers
- DevOps specialists
- AI architects