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 Duration 14 hours

Course Outline

Exploring the Architecture of Google Antigravity

  • Principles of agent-first design
  • The distinct functions of Editor and Manager interfaces
  • Workspace architecture and execution contexts

Configuring Agents and Their Capabilities

  • Allocating specific roles and specializations to agents
  • Establishing task boundaries and levels of autonomy
  • Governing security protocols and agent permissions

Architecting Multi-Agent Workflows

  • Strategic workflow planning and sequencing
  • Coordinating between 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 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

  • Guaranteeing traceability and transparency
  • Verifying the accuracy of agent outputs
  • Deploying safety mechanisms and failover strategies

Embedding Antigravity in Engineering Pipelines

  • Supporting CI/CD and release management workflows
  • Integrating with existing DevOps toolsets
  • Scaling agent tasks across teams and environments

Advanced Optimization for Multi-Agent Collaboration

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

Conclusion and Recommended Next Steps

Requirements

  • Knowledge of contemporary DevOps and platform engineering principles
  • Practical experience with AI-assisted development processes
  • Exposure to distributed systems or cloud-based environments

Target Audience

  • Platform engineers
  • DevOps engineers
  • AI architects

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