Advanced Antigravity: Feedback Loops, Learning & Long-Term Agent Memory Training Course

Understanding Antigravity’s Agent Architecture

• Internal representations and state models
• Layered behavior coordination
• Action generation pathways

Memory Systems for Long-Lived Agents

• Distinguishing short-term vs long-term memory behaviors
• Patterns for persistent knowledge storage
• Strategies to prevent memory corruption and drift

Feedback Loops and Behavior Shaping

• Human-in-the-loop feedback strategies
• Reinforcement mechanisms and reward adjustment
• Techniques for self-evaluation and self-correction

Learning Over Time

• Tracking agent learning progress
• Detecting and mitigating skill decay
• Adaptive updating based on operational context

Knowledge Base Construction and Retention

• Building structured long-term knowledge graphs
• Semantic retrieval and memory indexing
• Maintaining knowledge relevance and freshness

Agent Interactions and Multi-Agent Ecosystems

• Cooperative and competitive behaviors
• Collective memory and shared state
• Scaling emergent patterns across systems

Developer Feedback Integration

• Reviewing and annotating agent artifacts
• Automated evaluation pipelines
• Incorporating human judgment into learning loops

Advanced Optimization and Future Directions

• Performance tuning for long-duration tasks
• Predictive modeling of agent evolution
• Architectural trends and research frontiers

Summary and Next Steps