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Duration 14 hours
Course Outline
Comprehending Antigravity’s Agent Architecture
- Internal representations and state modeling
- Coordinated behavior across layers
- Pathways for action generation
Memory Systems for Long-Lived Agents
- Distinguishing short-term vs. 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 tuning
- Methods for self-evaluation and self-correction
Learning Over Time
- Monitoring agent learning progression
- Identifying and addressing skill degradation
- Contextual adaptive updates
Knowledge Base Construction and Retention
- Development of 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
- Shared memory and collective state
- Scaling emergent patterns across systems
Developer Feedback Integration
- Review and annotation of agent outputs
- Automation of evaluation pipelines
- Embedding human judgment into learning cycles
Advanced Optimization and Future Directions
- Performance tuning for extended-duration tasks
- Predictive modeling of agent evolution
- Architectural trends and research frontiers
Summary and Next Steps
Requirements
- A solid grasp of autonomous agent architectures
- Practical experience with large-scale AI systems
- Acquaintance with reinforcement learning principles
Audience
- Senior AI engineers
- Agent-platform architects
- R&D teams
