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 Duration 14 hours

Course Outline

Grasping the Architecture of Google Antigravity

  • Agent-first design philosophies
  • The distinct functions of Editor and Manager interfaces
  • Workspace organization and execution contexts

Setting Up Agents and Their Capabilities

  • Allocating agent roles and areas of specialization
  • Establishing task limits and autonomy parameters
  • Oversight of agent security and permission settings

Constructing 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
  • Analyzing graphs, states, and execution timelines
  • Intervening, overriding, or redirecting agent tasks

Creating and Managing Antigravity Artifacts

  • Task lists, strategic plans, and decision traces
  • Screenshots, browser recordings, and workspace snapshots
  • Audit logs and metadata for reproducibility

Verification and Quality Assurance Approaches

  • Maintaining traceability and transparency
  • Confirming the accuracy of agent outputs
  • Deploying safeguards and failover mechanisms

Embedding Antigravity into Engineering Pipelines

  • Facilitating CI/CD and release workflows
  • Integrating with current DevOps tooling
  • Scaling agent operations across teams and environments

Advanced Strategies for Multi-Agent Collaboration

  • Minimizing redundant actions and loops
  • Utilizing performance metrics and analytics
  • Creating resilient and adaptable workflow structures

Conclusion and Future Directions

Requirements

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

Target Audience

  • Platform engineers
  • DevOps engineers
  • AI architects

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