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Course Outline
Exploring the Architecture of Google Antigravity
- Agent-first design principles
- Functions of the Editor and Manager interfaces
- Workspace structure and execution contexts
Setting Up Agents and Capabilities
- Allocating agent roles and specializations
- Establishing task boundaries and autonomy levels
- Managing agent security and permissions
Building Multi-Agent Workflows
- Planning and sequencing workflows
- Coordinating background and foreground agents
- Applying chaining, delegation, and escalation patterns
Utilizing the Manager (Mission-Control) Interface
- Monitoring real-time agent activity
- Analyzing graphs, states, and execution timelines
- Intervening, overriding, or redirecting agent tasks
Creating and Managing Antigravity Artifacts
- Task lists, work plans, and decision traces
- Screenshots, browser recordings, and workspace captures
- Audit logs and reproducibility metadata
Verification and Quality Assurance Methods
- Maintaining traceability and transparency
- Assessing the accuracy of agent output
- Deploying safety measures and failover strategies
Integrating Antigravity into Engineering Pipelines
- Supporting CI/CD and release workflows
- Collaborating with established DevOps tools
- Scaling agent tasks across teams and environments
Advanced Optimization for Multi-Agent Collaboration
- Minimizing redundant actions and cycles
- Utilizing performance metrics and analytics
- Crafting resilient and adaptable workflows
Summary and Recommended Next Steps
Requirements
- Knowledge of contemporary DevOps and platform engineering concepts
- Practical experience with AI-assisted development workflows
- Familiarity with distributed systems or cloud environments
Target Audience
- Platform engineers
- DevOps engineers
- AI architects
14 Hours