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Duration 14 hours
Course Outline
Grasping Google Antigravity's Architecture
- Core principles of agent-first design
- The distinct functions of the Editor and Manager interfaces
- Workspace organization and execution contexts
Setting Up Agents and Capabilities
- Allocating agent roles and areas of specialization
- Establishing task boundaries and autonomy levels
- Administering agent security and permission settings
Constructing Multi-Agent Workflows
- Planning and sequencing workflow steps
- Coordinating background and foreground agent activities
- Applying chaining, delegation, and escalation patterns
Utilizing the Manager (Mission-Control) Interface
- Tracking live agent performance
- Analyzing graphs, states, and execution timelines
- Intervening to override or redirect agent tasks
Creating and Handling Antigravity Artifacts
- Task lists, strategic plans, and decision trails
- Screenshots, browser recordings, and workspace snapshots
- Audit logs and reproducibility metadata
Verification and Quality Assurance Approaches
- Maintaining traceability and transparency
- Verifying the accuracy of agent outputs
- Establishing safeguards and failover mechanisms
Integrating Antigravity into Engineering Pipelines
- Facilitating CI/CD and release management processes
- Collaborating with current DevOps toolsets
- Scaling agent tasks across various teams and environments
Advanced Optimization for Multi-Agent Collaboration
- Minimizing redundant actions and operational cycles
- Utilizing performance metrics and data analytics
- Engineering resilient and flexible workflows
Wrap-up and Future Steps
Requirements
- A solid grasp of contemporary DevOps and platform engineering principles
- Practical experience with AI-assisted development workflows
- Knowledge of distributed systems or cloud-based environments
Intended Audience
- Platform Engineers
- DevOps Engineers
- AI Architects