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 Duration 14 hours

Course Outline

Grasping the Architecture of Google Antigravity

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

Setting Up Agents and Their Capabilities

  • Allocating agent roles and specializations
  • Establishing task limits and autonomy tiers
  • Overseeing agent security and access permissions

Architecting Multi-Agent Workflows

  • Workflow strategy and sequencing
  • Synchronizing background and foreground agents
  • Applying chaining, delegation, and escalation models

Navigating the Manager (Mission-Control) Interface

  • Tracking live agent operations
  • Analyzing graphs, states, and execution timelines
  • Intervening, overriding, or redirecting agent assignments

Creating and Managing Antigravity Deliverables

  • Task inventories, work strategies, and decision trails
  • Screenshots, browser recordings, and workspace snapshots
  • Audit logs and reproducibility data

Verification and Quality Assurance Methods

  • Guaranteeing traceability and openness
  • Verifying the precision of agent outputs
  • Implementing safeguards and failover mechanisms

Embedding Antigravity into Engineering Pipelines

  • Backing CI/CD and release processes
  • Integrating with current DevOps tools
  • Expanding agent tasks across teams and environments

Advanced Optimization for Multi-Agent Cooperation

  • Minimizing duplicate actions and cycles
  • Utilizing performance metrics and analytics
  • Developing robust and flexible workflows

Wrap-up and Subsequent Actions

Requirements

  • Knowledge of contemporary DevOps and platform engineering principles
  • Practical experience with AI-empowered development cycles
  • Proficiency with distributed systems or cloud-based environments

Target Audience

  • Platform engineers
  • DevOps engineers
  • AI architects

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