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 Duration 14 hours

Course Outline

Fundamentals of Google Antigravity System Architecture

  • Principles of agent-centric system design
  • Functional distinctions between Editor and Manager interfaces
  • Organizing workspace structures and execution contexts

Agent Configuration and Capability Definition

  • Establishing agent roles and domain specializations
  • Setting operational boundaries and autonomy parameters
  • Administering security controls and access permissions for agents

Construction of Multi-Agent Workflows

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

Utilizing the Manager (Mission-Control) Interface

  • Oversight of live agent activities and performance
  • Analysis of dependency graphs, states, and execution timelines
  • Authority to intervene, override, or redirect agent tasks

Creation and Administration of Antigravity Artifacts

  • Maintaining task inventories, operational plans, and decision records
  • Managing screenshots, browser session recordings, and workspace snapshots
  • Reviewing audit logs and data related to reproducibility

Validation and Quality Assurance Protocols

  • Enforcing traceability and transparency in all operations
  • Verifying the accuracy and integrity of agent outputs
  • Deploying safety measures and failover mechanisms

Integration of Antigravity into Engineering Pipelines

  • Enhancing CI/CD processes and release management workflows
  • Interoperability with established DevOps tooling
  • Scaling agent-based tasks across multiple teams and environments

Advanced Strategies for Multi-Agent Collaboration

  • Minimizing redundant actions and operational cycles
  • Utilizing performance metrics and analytic data
  • Developing resilient and adaptive workflow architectures

Conclusions and Recommended Action Items

Requirements

  • Proficiency in modern DevOps and platform engineering concepts
  • Practical experience with AI-assisted development workflows
  • Working knowledge of distributed systems or cloud-based environments

Intended Audience

  • Platform engineers
  • DevOps engineers
  • AI architects

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