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Course Outline
Understanding Google Antigravity's Architecture
- Core agent-first design principles.
- The distinct roles of the Editor and Manager interfaces.
- Workspace structure and execution contexts.
Configuring Agents and Capabilities
- Assigning specific agent roles and specializations.
- Defining task boundaries and levels of autonomy.
- Managing security protocols and permissions for agents.
Designing Multi-Agent Workflows
- Planning and sequencing workflows effectively.
- Coordinating between background and foreground agents.
- Applying chaining, delegation, and escalation patterns.
Utilizing the Manager (Mission-Control) Interface
- Monitoring live agent activity in real-time.
- Interpreting graphs, states, and execution timelines.
- Intervening, overriding, or redirecting agent tasks when necessary.
Generating and Managing Antigravity Artifacts
- Working with task lists, work plans, and decision traces.
- Utilizing screenshots, browser recordings, and workspace captures.
- Accessing 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 effectively with existing DevOps tools.
- Scaling agent tasks across diverse teams and environments.
Advanced Optimization for Multi-Agent Collaboration
- Reducing redundant actions and operational 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.
- Practical experience with AI-assisted development workflows.
- Familiarity with distributed systems or cloud environments.
Target Audience
- Platform engineers.
- DevOps engineers.
- AI architects.
14 Hours
