Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Exploring Google Antigravity's Architecture
- Agent-first design principles
- Functions of the Editor and Manager interfaces
- Workspace structure and execution contexts
Configuring Agents and Capabilities
- Assigning specific roles and specializations to agents
- Setting task boundaries and autonomy levels
- Administering security and permissions for agents
Designing Multi-Agent Workflows
- Planning and sequencing workflow steps
- Coordinating background and foreground agents
- Applying chaining, delegation, and escalation patterns
Utilizing the Manager (Mission-Control) Interface
- Monitoring live agent activity
- Interpreting graphs, states, and execution timelines
- Intervening, overriding, or redirecting agent tasks
Generating 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 existing DevOps tools
- Scaling agent tasks across teams and environments
Advanced Optimization for Multi-Agent Collaboration
- Reducing 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 principles.
- Practical experience with AI-assisted development workflows.
- Familiarity with distributed systems or cloud-based environments.
Target Audience
- Platform engineers
- DevOps engineers
- AI architects