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Course Outline
Understanding Google Antigravity's Architecture
- Principles of agent-first design.
- The 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 security settings and permissions for agents.
Designing Multi-Agent Workflows
- Planning and sequencing workflows.
- Coordinating between background and foreground agents.
- Utilizing patterns for chaining, delegation, and escalation.
Working with the Manager (Mission-Control) Interface
- Monitoring live agent activity.
- Interpreting graphs, states, and execution timelines.
- Intervening, overriding, or redirecting agent tasks when necessary.
Generating and Managing Antigravity Artifacts
- Task lists, work plans, and decision traces.
- Screenshots, browser recordings, and workspace captures.
- Audit logs and metadata for reproducibility.
Verification and Quality Assurance Techniques
- Ensuring traceability and transparency.
- 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 tools.
- Scaling agent tasks across teams and environments.
Advanced Optimization for Multi-Agent Collaboration
- Reducing redundant actions and processing cycles.
- Leveraging performance metrics and analytics.
- Designing resilient and adaptable workflows.
Summary and Next Steps
Requirements
- Knowledge of modern DevOps and platform engineering concepts.
- Experience with AI-assisted development workflows.
- Familiarity with distributed systems or cloud environments.
Target Audience
- Platform engineers.
- DevOps engineers.
- AI architects.
14 Hours
