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 Duration 14 hours

Course Outline

Exploring Google Antigravity's Architecture

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

Configuring Agents and Capabilities

  • Allocating agent roles and specialisations
  • Establishing task boundaries and levels of autonomy
  • Administering agent security and permissions

Architecting Multi-Agent Workflows

  • Strategic workflow planning and sequencing
  • Coordinating background and foreground agents
  • Applying chaining, delegation and escalation patterns

Utilising the Manager (Mission-Control) Interface

  • Monitoring live agent activity
  • Interpreting graphs, states and execution timelines
  • Intervening, overriding or redirecting agent tasks

Creating and Managing Antigravity Artifacts

  • Task lists, work plans and decision traces
  • Screenshots, browser recordings and workspace captures
  • Audit logs and reproducibility metadata

Verification and Quality Assurance Techniques

  • Ensuring traceability and transparency
  • Validating the accuracy of agent output
  • Implementing safeguards and failover strategies

Integrating Antigravity into Engineering Pipelines

  • Supporting CI/CD and release workflows
  • Collaborating with established DevOps tools
  • 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 contemporary DevOps and platform engineering principles
  • Practical experience with AI-assisted development workflows
  • Familiarity with distributed systems or cloud-based environments

Target Audience

  • Platform engineers
  • DevOps engineers
  • AI architects

Number of participants


Price per participant

Provisional Upcoming Courses (Require 5+ participants)

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