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Duration 14 hours
Course Outline
Exploring the Architecture of Google Antigravity
- Core principles of agent-first design
- The specific roles of the Editor and Manager interfaces
- Workspace organization and execution contexts
Configuring Agents and Their Capabilities
- Assigning specific roles and specializations to agents
- Setting task boundaries and defining levels of autonomy
- Overseeing security protocols and agent permissions
Architecting Multi-Agent Workflows
- Strategic planning and workflow sequencing
- Coordinating background and foreground agent activities
- Applying chaining, delegation, and escalation patterns
Utilizing the Manager (Mission-Control) Interface
- Monitoring real-time agent performance
- Analyzing graphs, states, and execution timelines
- Intervening, overriding, or redirecting agent tasks
Creating and Managing Antigravity Artifacts
- Managing task lists, work plans, and decision trails
- Capturing screenshots, browser recordings, and workspace states
- Maintaining audit logs and reproducibility metadata
Verification and Quality Assurance Methods
- Ensuring full traceability and transparency
- Verifying the accuracy of agent outputs
- Establishing safeguards and failover strategies
Integrating Antigravity into Engineering Pipelines
- Facilitating CI/CD and release workflows
- Collaborating with existing DevOps tooling
- Scaling agent tasks across teams and various environments
Advanced Optimization for Multi-Agent Collaboration
- Minimizing redundant actions and cycles
- Utilizing performance metrics and analytics
- Designing resilient and adaptable workflows
Conclusion and Next Steps
Requirements
- A solid grasp of modern DevOps and platform engineering principles
- Practical experience with AI-assisted development processes
- Knowledge of distributed systems or cloud-based environments
Target Audience
- Platform engineers
- DevOps engineers
- AI architects