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Duration 14 hours
Course Outline
Exploring Google Antigravity's Structural Design
- Core principles of agent-first architecture
- Functions of the Editor and Manager interfaces
- Organization of workspaces and execution environments
Setting Up Agents and Defining Capabilities
- Distributing agent roles and areas of specialization
- Establishing task limits and levels of autonomy
- Controlling security protocols and agent permissions
Architecting Multi-Agent Workflows
- Strategic planning and workflow sequencing
- Synchronizing background and foreground agents
- Applying chaining, delegation, and escalation methods
Navigating the Manager (Mission-Control) Interface
- Tracking live agent activities
- Analyzing graphs, states, and execution timelines
- Stepping in to override or redirect agent tasks
Producing and Handling Antigravity Artifacts
- Task lists, work strategies, and decision trails
- Screen captures, browser recordings, and workspace snapshots
- Audit logs and reproducibility metadata
Verification and Quality Assurance Methods
- Maintaining traceability and clarity in processes
- Checking the precision of agent outputs
- Deploying safeguards and failover mechanisms
Incorporating Antigravity into Engineering Pipelines
- Facilitating CI/CD and release cycles
- Integrating with current DevOps toolchains
- Scaling agent tasks across various teams and environments
Advanced Refinement for Multi-Agent Collaboration
- Minimizing redundant actions and loops
- Utilizing performance data and analytics
- Crafting resilient and flexible workflows
Recap and Future Directions
Requirements
- A solid grasp of contemporary DevOps and platform engineering principles
- Hands-on experience with AI-assisted development processes
- Knowledge of distributed systems or cloud-based environments
Intended Audience
- Platform engineers
- DevOps engineers
- AI architects