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 Duration 14 hours

Course Outline

Grasping the Architecture of Google Antigravity

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

Configuring Agent Roles and Abilities

  • Assigning specific roles and areas of expertise to agents
  • Establishing task limits and levels of autonomy
  • Overseeing security protocols and permission sets for agents

Architecting Multi-Agent Workflows

  • Planning workflow sequences and dependencies
  • Harmonizing background and foreground agent activities
  • Applying chaining, delegation, and escalation techniques

Utilizing the Manager (Mission-Control) Interface

  • Observing real-time agent behavior
  • Analyzing graphs, states, and execution timelines
  • Intervening, overriding, or redirecting agent tasks as needed

Creating and Handling Antigravity Deliverables

  • Task lists, work plans, and decision trails
  • Visual captures, browser recordings, and workspace snapshots
  • Audit logs and metadata for reproducibility

Verification and Quality Assurance Practices

  • Guaranteeing full traceability and transparency
  • Checking the accuracy of agent outputs
  • Implementing safety mechanisms and failover strategies

Embedding Antigravity into Engineering Pipelines

  • Enhancing CI/CD and release processes
  • Interoperating with current DevOps toolsets
  • Scaling agent tasks across various teams and environments

Advanced Refinement for Multi-Agent Collaboration

  • Minimizing redundant actions and cycles
  • Utilizing performance metrics and analytics
  • Crafting resilient and flexible workflow structures

Conclusion and Recommended Next Steps

Requirements

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

Target Audience

  • Platform engineers
  • DevOps engineers
  • AI architects

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