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Course Outline

Exploring the Architecture of Google Antigravity

  • Agent-first design principles
  • Functions of the Editor and Manager interfaces
  • Workspace structure and execution contexts

Setting Up Agents and Capabilities

  • Allocating agent roles and specializations
  • Establishing task boundaries and autonomy levels
  • Managing agent security and permissions

Building Multi-Agent Workflows

  • Planning and sequencing workflows
  • Coordinating background and foreground agents
  • Applying chaining, delegation, and escalation patterns

Utilizing the Manager (Mission-Control) Interface

  • Monitoring real-time agent activity
  • Analyzing graphs, states, and execution timelines
  • Intervening, 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 reproducibility metadata

Verification and Quality Assurance Methods

  • Maintaining traceability and transparency
  • Assessing the accuracy of agent output
  • Deploying safety measures and failover strategies

Integrating Antigravity into Engineering Pipelines

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

Advanced Optimization for Multi-Agent Collaboration

  • Minimizing redundant actions and cycles
  • Utilizing performance metrics and analytics
  • Crafting resilient and adaptable workflows

Summary and Recommended Next Steps

Requirements

  • Knowledge of contemporary 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
 14 Hours

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