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 Duration 14 hours

Course Outline

Exploring Google Antigravity's Structural Design

  • Core principles of agent-first architecture
  • Functions of the Editor and Manager interfaces
  • Organization of workspaces and execution environments

Setting Up Agents and Defining Capabilities

  • Distributing agent roles and areas of specialization
  • Establishing task limits and levels of autonomy
  • Controlling security protocols and agent permissions

Architecting Multi-Agent Workflows

  • Strategic planning and workflow sequencing
  • Synchronizing background and foreground agents
  • Applying chaining, delegation, and escalation methods

Navigating the Manager (Mission-Control) Interface

  • Tracking live agent activities
  • Analyzing graphs, states, and execution timelines
  • Stepping in to override or redirect agent tasks

Producing and Handling Antigravity Artifacts

  • Task lists, work strategies, and decision trails
  • Screen captures, browser recordings, and workspace snapshots
  • Audit logs and reproducibility metadata

Verification and Quality Assurance Methods

  • Maintaining traceability and clarity in processes
  • Checking the precision of agent outputs
  • Deploying safeguards and failover mechanisms

Incorporating Antigravity into Engineering Pipelines

  • Facilitating CI/CD and release cycles
  • Integrating with current DevOps toolchains
  • Scaling agent tasks across various teams and environments

Advanced Refinement for Multi-Agent Collaboration

  • Minimizing redundant actions and loops
  • Utilizing performance data and analytics
  • Crafting resilient and flexible workflows

Recap and Future Directions

Requirements

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

Intended Audience

  • Platform engineers
  • DevOps engineers
  • AI architects

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