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 Duration 14 hours

Course Outline

Exploring Google Antigravity's Architecture

  • Agent-first design principles
  • The roles of 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 protocols and permissions for agents

Designing Multi-Agent Workflows

  • Planning and sequencing workflow steps
  • Coordinating background and foreground agents
  • Utilizing chaining, delegation, and escalation patterns

Navigating 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 the accuracy of agent output
  • 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
  • Practical experience with AI-assisted development workflows
  • Familiarity with distributed systems or cloud environments

Target Audience

  • Platform engineers
  • DevOps engineers
  • AI architects

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