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Course Outline
Grasping the Architecture of Google Antigravity
- Agent-first design principles
- The distinct roles of the Editor and Manager interfaces
- Workspace structure and execution contexts
Configuring Agents and Capabilities
- Assigning specific roles and specializations to agents
- Defining task boundaries and levels of autonomy
- Managing agent security and permissions
Designing Multi-Agent Workflows
- Planning and sequencing workflow steps
- Coordinating background and foreground agents
- Utilising chaining, delegation, and escalation patterns
Navigating the Manager (Mission-Control) Interface
- Monitoring live agent activity in real time
- Interpreting graphs, states, and execution timelines
- Intervening to override or redirect agent tasks
Generating and Managing Antigravity Artifacts
- Reviewing task lists, work plans, and decision traces
- Analysing screenshots, browser recordings, and workspace captures
- Managing audit logs and reproducibility metadata
Verification and Quality Assurance Techniques
- Ensuring traceability and transparency in processes
- Validating the accuracy of agent outputs
- Implementing safeguards and failover strategies
Integrating Antigravity into Engineering Pipelines
- Supporting CI/CD and release workflows
- Collaborating with existing DevOps toolchains
- Scaling agent tasks across teams and environments
Advanced Optimisation for Multi-Agent Collaboration
- Minimising redundant actions and cycles
- Leveraging performance metrics and analytics
- Designing resilient and adaptable workflows
Summary and Next Steps
Requirements
- A solid grasp of modern 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