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Duration 14 hours
Course Outline
Exploring Antigravity’s Agent Architecture
- Internal representations and state modeling
- Coordination of layered behaviors
- Pathways for action generation
Memory Systems for Long-Lived Agents
- Contrasting short-term and long-term memory dynamics
- Patterns for persistent knowledge storage
- Strategies to prevent memory corruption and drift
Feedback Loops and Behavior Shaping
- Human-in-the-loop feedback approaches
- Reinforcement mechanisms and reward calibration
- Techniques for self-evaluation and self-correction
Learning Over Time
- Monitoring agent learning progress
- Identifying and addressing skill decay
- Adaptive updates driven by operational context
Knowledge Base Construction and Retention
- Developing structured long-term knowledge graphs
- Semantic retrieval and memory indexing methods
- Ensuring knowledge relevance and currency
Agent Interactions and Multi-Agent Ecosystems
- Cooperative versus competitive behaviors
- Collective memory and shared state management
- Scaling emergent patterns across systems
Developer Feedback Integration
- Reviewing and annotating agent outputs
- Automated evaluation workflows
- Incorporating human insights into learning cycles
Advanced Optimization and Future Directions
- Optimizing performance for extended tasks
- Predictive modeling of agent evolution
- Emerging architectural trends and research frontiers
Summary and Next Steps
Requirements
- Knowledge of autonomous agent architectures
- Experience working with large-scale AI systems
- Understanding of reinforcement learning principles
Target Audience
- Senior AI engineers
- Architects of agent platforms
- R&D teams
