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Course Outline
Understanding Google Antigravity's Architecture
- Agent-first design principles.
- Roles of the Editor and Manager interfaces.
- Workspace structure and execution contexts.
Configuring Agents and Capabilities
- Assigning agent roles and specializations.
- Defining task boundaries and autonomy levels.
- Managing security and permissions for agents.
Designing Multi-Agent Workflows
- Workflow planning and sequencing.
- Coordinating background and foreground agents.
- Using chaining, delegation, and escalation patterns.
Working with the Manager (Mission-Control) Interface
- Monitoring live agent activity.
- Interpreting graphs, states, and execution timelines.
- Intervening, overriding, or redirecting agent tasks.
Generating 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 agent output accuracy.
- Implementing safe-guards and failover strategies.
Integrating Antigravity into Engineering Pipelines
- Supporting CI/CD and release workflows.
- Collaborating with existing DevOps tools.
- Scaling agent tasks across teams and environments.
Advanced Optimization for Multi-Agent Collaboration
- Reducing redundant actions and cycles.
- Leveraging performance metrics and analytics.
- Designing resilient and adaptable workflows.
Summary and Next Steps
Requirements
- A solid understanding of modern DevOps and platform engineering concepts.
- Prior experience with AI-assisted development workflows.
- Familiarity with distributed systems or cloud environments.
Audience
- Platform engineers.
- DevOps engineers.
- AI architects.
14 Hours
