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Duration 14 hours
Course Outline
Comprehending Antigravity’s Agent Architecture
- Internal data representations and state models
- Coordination of layered behaviors
- Pathways for action generation
Memory Systems for Long-Lived Agents
- Distinction between 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 strategies
- Reinforcement mechanisms and reward tuning
- Methods for self-evaluation and self-correction
Learning Over Time
- Monitoring agent learning progress
- Identifying and reducing skill decay
- Adaptive updates driven by operational context
Knowledge Base Construction and Retention
- Developing structured long-term knowledge graphs
- Semantic retrieval and memory indexing techniques
- Preserving knowledge relevance and currency
Agent Interactions and Multi-Agent Ecosystems
- Cooperative versus competitive behaviors
- Shared state and collective memory
- Scaling emergent patterns across systems
Developer Feedback Integration
- Reviewing and annotating agent outputs
- Automated evaluation pipelines
- Embedding human judgment into learning cycles
Advanced Optimization and Future Directions
- Performance tuning for long-duration tasks
- Predictive modeling of agent evolution
- Emerging architectural trends and research frontiers
Summary and Next Steps
Requirements
- Comprehension of autonomous agent architectures
- Professional experience with large-scale AI systems
- Acknowledgment of reinforcement learning principles
Target Audience
- Senior AI engineers
- Architects of agent platforms
- R&D teams
