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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