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 Duration 14 hours

Course Outline

Grasping Google Antigravity's Architectural Framework

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

Setting Up Agents and Capabilities

  • Distributing agent roles and specific specializations
  • Establishing task boundaries and levels of autonomy
  • Regulating agent security and access permissions

Architecting Multi-Agent Workflows

  • Strategic workflow planning and task sequencing
  • Synchronizing background and foreground agents
  • Applying patterns for chaining, delegation, and escalation

Utilizing the Manager (Mission-Control) Interface

  • Observing real-time agent activities
  • Analyzing graphs, states, and execution timelines
  • Stepping in to override or redirect agent tasks

Producing and Managing Antigravity Artifacts

  • Reviewing task lists, work plans, and decision trails
  • Managing screenshots, browser recordings, and workspace snapshots
  • Maintaining audit logs and reproducibility metadata

Verification and Quality Assurance Methods

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

Embedding Antigravity into Engineering Pipelines

  • Enhancing CI/CD and release management processes
  • Interoperating with current DevOps tooling
  • Expanding agent tasks across various teams and environments

Advanced Strategies for Multi-Agent Collaboration

  • Minimizing redundant actions and feedback cycles
  • Utilizing performance metrics and data analytics
  • Crafting robust and adaptable workflows

Conclusion and Future Directions

Requirements

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

Intended Audience

  • Platform engineers
  • DevOps engineers
  • AI architects

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