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Duration 14 hours
Course Outline
Grasping Google Antigravity's Architectural Framework
- Core principles of agent-first design
- Functions of the Editor and Manager interfaces
- Workspace organization and execution contexts
Setting Up Agents and Capabilities
- Distributing agent roles and specific specializations
- Establishing task boundaries and levels of autonomy
- Regulating agent security and access permissions
Architecting Multi-Agent Workflows
- Strategic workflow planning and task sequencing
- Synchronizing background and foreground agents
- Applying patterns for chaining, delegation, and escalation
Utilizing the Manager (Mission-Control) Interface
- Observing real-time agent activities
- Analyzing graphs, states, and execution timelines
- Stepping in to override or redirect agent tasks
Producing and Managing Antigravity Artifacts
- Reviewing task lists, work plans, and decision trails
- Managing screenshots, browser recordings, and workspace snapshots
- Maintaining audit logs and reproducibility metadata
Verification and Quality Assurance Methods
- Safeguarding traceability and operational transparency
- Confirming the accuracy of agent outputs
- Deploying safeguards and failover mechanisms
Embedding Antigravity into Engineering Pipelines
- Enhancing CI/CD and release management processes
- Interoperating with current DevOps tooling
- Expanding agent tasks across various teams and environments
Advanced Strategies for Multi-Agent Collaboration
- Minimizing redundant actions and feedback cycles
- Utilizing performance metrics and data analytics
- Crafting robust and adaptable workflows
Conclusion and Future Directions
Requirements
- A solid grasp of contemporary DevOps and platform engineering concepts
- Practical experience with AI-assisted development processes
- Knowledge of distributed systems or cloud computing environments
Intended Audience
- Platform engineers
- DevOps engineers
- AI architects