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

  • An understanding of modern DevOps and platform engineering concepts
  • Experience with AI-assisted development workflows
  • Familiarity with distributed systems or cloud environments

Audience

  • Platform engineers
  • DevOps engineers
  • AI architects
 14 Hours

Delivery Options

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