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Duration 14 hours
Course Outline
Exploring Google Antigravity's Architecture
- Core principles of agent-first design
- Functions of the Editor and Manager interfaces
- Workspace configuration and execution contexts
Configuring Agents and Capabilities
- Allocating agent roles and specialisations
- Establishing task boundaries and levels of autonomy
- Administering agent security and permissions
Architecting Multi-Agent Workflows
- Strategic workflow planning and sequencing
- Coordinating background and foreground agents
- Applying chaining, delegation and escalation patterns
Utilising the Manager (Mission-Control) Interface
- Monitoring live agent activity
- Interpreting 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 Techniques
- Ensuring traceability and transparency
- Validating the accuracy of agent output
- Implementing safeguards 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 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 contemporary DevOps and platform engineering principles
- Practical experience with AI-assisted development workflows
- Familiarity with distributed systems or cloud-based environments
Target Audience
- Platform engineers
- DevOps engineers
- AI architects