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

Grasping Antigravity’s Agent Architecture

• Internal representations and state models
• Coordinated behaviour across layers
• Pathways for action generation

Memory Systems for Long-Lived Agents

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

Feedback Loops and Behaviour Shaping

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

Learning Over Time

• Monitoring agent learning progress
• Identifying and mitigating skill decay
• Adaptive updates driven by operational context

Knowledge Base Construction and Retention

• Developing structured long-term knowledge graphs
• Semantic retrieval and memory indexing
• Preserving knowledge relevance and currency

Agent Interactions and Multi-Agent Ecosystems

• Cooperative and competitive dynamics
• Collective memory and shared states
• Scaling emergent patterns across systems

Developer Feedback Integration

• Reviewing and annotating agent outputs
• Automated evaluation pipelines
• Integrating human judgement into learning loops

Advanced Optimisation and Future Directions

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

Summary and Next Steps