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Course Outline

Grasping the Architecture of Google Antigravity

  • Agent-first design principles
  • The distinct roles of the Editor and Manager interfaces
  • Workspace structure and execution contexts

Configuring Agents and Capabilities

  • Assigning specific roles and specializations to agents
  • Defining task boundaries and levels of autonomy
  • Managing agent security and permissions

Designing Multi-Agent Workflows

  • Planning and sequencing workflow steps
  • Coordinating background and foreground agents
  • Utilising chaining, delegation, and escalation patterns

Navigating the Manager (Mission-Control) Interface

  • Monitoring live agent activity in real time
  • Interpreting graphs, states, and execution timelines
  • Intervening to override or redirect agent tasks

Generating and Managing Antigravity Artifacts

  • Reviewing task lists, work plans, and decision traces
  • Analysing screenshots, browser recordings, and workspace captures
  • Managing audit logs and reproducibility metadata

Verification and Quality Assurance Techniques

  • Ensuring traceability and transparency in processes
  • Validating the accuracy of agent outputs
  • Implementing safeguards and failover strategies

Integrating Antigravity into Engineering Pipelines

  • Supporting CI/CD and release workflows
  • Collaborating with existing DevOps toolchains
  • Scaling agent tasks across teams and environments

Advanced Optimisation for Multi-Agent Collaboration

  • Minimising redundant actions and cycles
  • Leveraging performance metrics and analytics
  • Designing resilient and adaptable workflows

Summary and Next Steps

Requirements

  • A solid grasp of modern DevOps and platform engineering concepts
  • Practical experience with AI-assisted development workflows
  • Familiarity with distributed systems or cloud environments

Target Audience

  • Platform engineers
  • DevOps engineers
  • AI architects
 14 Hours

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