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Duration 14 hours
Course Outline
Grasping 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 Their Capabilities
- Allocating agent roles and specializations
- Establishing task limits and autonomy tiers
- Overseeing agent security and access permissions
Architecting Multi-Agent Workflows
- Workflow strategy and sequencing
- Synchronizing background and foreground agents
- Applying chaining, delegation, and escalation models
Navigating the Manager (Mission-Control) Interface
- Tracking live agent operations
- Analyzing graphs, states, and execution timelines
- Intervening, overriding, or redirecting agent assignments
Creating and Managing Antigravity Deliverables
- Task inventories, work strategies, and decision trails
- Screenshots, browser recordings, and workspace snapshots
- Audit logs and reproducibility data
Verification and Quality Assurance Methods
- Guaranteeing traceability and openness
- Verifying the precision of agent outputs
- Implementing safeguards and failover mechanisms
Embedding Antigravity into Engineering Pipelines
- Backing CI/CD and release processes
- Integrating with current DevOps tools
- Expanding agent tasks across teams and environments
Advanced Optimization for Multi-Agent Cooperation
- Minimizing duplicate actions and cycles
- Utilizing performance metrics and analytics
- Developing robust and flexible workflows
Wrap-up and Subsequent Actions
Requirements
- Knowledge of contemporary DevOps and platform engineering principles
- Practical experience with AI-empowered development cycles
- Proficiency with distributed systems or cloud-based environments
Target Audience
- Platform engineers
- DevOps engineers
- AI architects