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Course Outline

Comprehending the Architecture of Google Antigravity

  • Agent-first design principles
  • The specific roles of Editor and Manager interfaces
  • Workspace organization and execution contexts

Agent and Capability Configuration

  • Assigning roles and areas of specialization to agents
  • Establishing task boundaries and levels of autonomy
  • Governing agent security and permissions

Designing Multi-Agent Workflows

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

Utilizing the Manager (Mission-Control) Interface

  • Tracking live agent activities
  • Interpreting graphs, states, and execution timelines
  • Intervening to override or redirect agent tasks

Creation and Management of Antigravity Artifacts

  • Task lists, work plans, and decision traces
  • Screenshots, browser recordings, and workspace captures
  • Audit logs and reproducibility metadata

Verification and Quality Assurance Methods

  • Maintaining traceability and transparency
  • Validating the accuracy of agent outputs
  • Implementing safeguards and failover strategies

Integrating Antigravity into Engineering Pipelines

  • Supporting CI/CD and release processes
  • Collaborating with established DevOps tools
  • Scaling agent tasks across various teams and environments

Advanced Optimization for Multi-Agent Collaboration

  • Minimizing redundant actions and cycles
  • Leveraging performance metrics and analytics
  • Designing resilient and adaptable workflows

Summary and Next Steps

Requirements

  • A solid grasp of contemporary DevOps and platform engineering concepts
  • Hands-on experience with AI-assisted development workflows
  • Working knowledge of distributed systems or cloud environments

Target Audience

  • Platform engineers
  • DevOps engineers
  • AI architects
 14 Hours

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