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Duration 14 hours
Course Outline
Exploring the Architecture of Google Antigravity
- Principles of agent-first design
- The distinct functions of Editor and Manager interfaces
- Workspace architecture and execution contexts
Configuring Agents and Their Capabilities
- Allocating specific roles and specializations to agents
- Establishing task boundaries and levels of autonomy
- Governing security protocols and agent permissions
Architecting Multi-Agent Workflows
- Strategic workflow planning and sequencing
- Coordinating between 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 in, 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 metadata for reproducibility
Verification and Quality Assurance Methods
- Guaranteeing traceability and transparency
- Verifying the accuracy of agent outputs
- Deploying safety mechanisms and failover strategies
Embedding Antigravity in Engineering Pipelines
- Supporting CI/CD and release management workflows
- Integrating with existing DevOps toolsets
- Scaling agent tasks across teams and environments
Advanced Optimization for Multi-Agent Collaboration
- Minimizing redundant actions and cycles
- Utilizing performance metrics and analytics
- Constructing resilient and adaptable workflows
Conclusion and Recommended Next Steps
Requirements
- Knowledge of contemporary DevOps and platform engineering principles
- Practical experience with AI-assisted development processes
- Exposure to distributed systems or cloud-based environments
Target Audience
- Platform engineers
- DevOps engineers
- AI architects