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Duration 14 hours
Course Outline
Grasping the Architecture of Google Antigravity
- Core principles of agent-first design
- The distinct roles of the Editor and Manager interfaces
- Workspace organization and execution contexts
Configuring Agent Roles and Abilities
- Assigning specific roles and areas of expertise to agents
- Establishing task limits and levels of autonomy
- Overseeing security protocols and permission sets for agents
Architecting Multi-Agent Workflows
- Planning workflow sequences and dependencies
- Harmonizing background and foreground agent activities
- Applying chaining, delegation, and escalation techniques
Utilizing the Manager (Mission-Control) Interface
- Observing real-time agent behavior
- Analyzing graphs, states, and execution timelines
- Intervening, overriding, or redirecting agent tasks as needed
Creating and Handling Antigravity Deliverables
- Task lists, work plans, and decision trails
- Visual captures, browser recordings, and workspace snapshots
- Audit logs and metadata for reproducibility
Verification and Quality Assurance Practices
- Guaranteeing full traceability and transparency
- Checking the accuracy of agent outputs
- Implementing safety mechanisms and failover strategies
Embedding Antigravity into Engineering Pipelines
- Enhancing CI/CD and release processes
- Interoperating with current DevOps toolsets
- Scaling agent tasks across various teams and environments
Advanced Refinement for Multi-Agent Collaboration
- Minimizing redundant actions and cycles
- Utilizing performance metrics and analytics
- Crafting resilient and flexible workflow structures
Conclusion and Recommended Next Steps
Requirements
- A solid grasp of contemporary DevOps and platform engineering methodologies
- Practical experience with AI-enhanced development processes
- Knowledge of distributed systems or cloud-based environments
Target Audience
- Platform engineers
- DevOps engineers
- AI architects