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Course Outline
Comprehending the Architecture of Google Antigravity
- Core principles of agent-first design
- The functions of the Editor and Manager interfaces
- Workspace structure and execution contexts
Setting Up Agents and Capabilities
- Defining agent roles and areas of specialization
- Establishing task boundaries and levels of autonomy
- Managing security settings and permissions for agents
Crafting 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 real-time agent activity
- Analyzing graphs, states, and execution timelines
- Intervening in, 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 metadata for reproducibility
Verification and Quality Assurance Methods
- Ensuring traceability and transparency
- Validating the accuracy of agent outputs
- Implementing safeguards and failover strategies
Incorporating Antigravity into Engineering Pipelines
- Facilitating CI/CD and release workflows
- Collaborating with existing DevOps tools
- Expanding agent tasks across teams and environments
Advanced Optimization for Multi-Agent Collaboration
- Minimizing redundant actions and cycles
- Utilizing performance metrics and analytics
- Designing resilient and adaptable workflows
Summary and Future Steps
Requirements
- A solid grasp of modern DevOps and platform engineering concepts
- Hands-on experience with AI-assisted development workflows
- Familiarity with cloud environments or distributed systems
Target Audience
- Platform engineers
- DevOps engineers
- AI architects
14 Hours
