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Duration 14 hours
Course Outline
Exploring the Architecture of Google Antigravity
- Core principles of agent-first design
- Functions of the Editor and Manager interfaces
- Workspace organization and execution contexts
Setting Up Agents and Defining Capabilities
- Allocating specific roles and areas of specialization to agents
- Establishing task boundaries and levels of autonomy
- Controlling security protocols and agent permissions
Architecting Multi-Agent Workflows
- Strategic planning and sequence management
- Synchronizing background and foreground agents
- Applying patterns for chaining, delegation, and escalation
Utilizing the Manager (Mission-Control) Interface
- Monitoring real-time agent activity
- Analyzing graphs, states, and execution timelines
- Intervening to override or redirect agent tasks
Creating and Managing Antigravity Artifacts
- Reviewing task lists, work plans, and decision traces
- Examining screenshots, browser recordings, and workspace captures
- Accessing audit logs and reproducibility metadata
Applying Verification and Quality Assurance Techniques
- Maintaining full traceability and transparency
- Validating the accuracy of agent outputs
- Deploying safeguards and failover strategies
Integrating Antigravity into Engineering Pipelines
- Enhancing CI/CD and release processes
- Interoperating with existing DevOps tools
- Scaling agent tasks across various teams and environments
Advanced Strategies for Multi-Agent Collaboration
- Minimizing redundant actions and cycles
- Leveraging performance metrics and analytics
- Constructing resilient and adaptable workflows
Concluding Summary and Next Steps
Requirements
- A solid grasp of contemporary DevOps and platform engineering principles
- Prior experience with AI-assisted development processes
- Proficiency with distributed systems or cloud-based environments
Target Audience
- Platform engineers
- DevOps engineers
- AI architects