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 Duration 14 hours

Course Outline

Grasping Google Antigravity's Architecture

  • Core principles of agent-first design
  • The distinct functions of the Editor and Manager interfaces
  • Workspace organization and execution contexts

Setting Up Agents and Capabilities

  • Allocating agent roles and areas of specialization
  • Establishing task boundaries and autonomy levels
  • Administering agent security and permission settings

Constructing Multi-Agent Workflows

  • Planning and sequencing workflow steps
  • Coordinating background and foreground agent activities
  • Applying chaining, delegation, and escalation patterns

Utilizing the Manager (Mission-Control) Interface

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

Creating and Handling Antigravity Artifacts

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

Verification and Quality Assurance Approaches

  • Maintaining traceability and transparency
  • Verifying the accuracy of agent outputs
  • Establishing safeguards and failover mechanisms

Integrating Antigravity into Engineering Pipelines

  • Facilitating CI/CD and release management processes
  • Collaborating with current DevOps toolsets
  • Scaling agent tasks across various teams and environments

Advanced Optimization for Multi-Agent Collaboration

  • Minimizing redundant actions and operational cycles
  • Utilizing performance metrics and data analytics
  • Engineering resilient and flexible workflows

Wrap-up and Future Steps

Requirements

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

Intended Audience

  • Platform Engineers
  • DevOps Engineers
  • AI Architects

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