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Duration 14 hours
Course Outline
Grasping the Architecture of Google Antigravity
- Agent-first design philosophies
- The distinct functions of Editor and Manager interfaces
- Workspace organization and execution contexts
Setting Up Agents and Their Capabilities
- Allocating agent roles and areas of specialization
- Establishing task limits and autonomy parameters
- Oversight of agent security and permission settings
Constructing 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
- Analyzing graphs, states, and execution timelines
- Intervening, overriding, or redirecting agent tasks
Creating and Managing Antigravity Artifacts
- Task lists, strategic plans, and decision traces
- Screenshots, browser recordings, and workspace snapshots
- Audit logs and metadata for reproducibility
Verification and Quality Assurance Approaches
- Maintaining traceability and transparency
- Confirming the accuracy of agent outputs
- Deploying safeguards and failover mechanisms
Embedding Antigravity into Engineering Pipelines
- Facilitating CI/CD and release workflows
- Integrating with current DevOps tooling
- Scaling agent operations across teams and environments
Advanced Strategies for Multi-Agent Collaboration
- Minimizing redundant actions and loops
- Utilizing performance metrics and analytics
- Creating resilient and adaptable workflow structures
Conclusion and Future Directions
Requirements
- A grasp of contemporary 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