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Duration 14 hours
Course Outline
Comprehending Antigravity’s Agent Architecture
- Internal representations and state models
- Coordinating layered behaviors
- Pathways for action generation
Memory Systems for Long-Lived Agents
- Distinctions between short-term and 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 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
- Creating structured long-term knowledge graphs
- Implementing semantic retrieval and memory indexing
- Preserving knowledge relevance and currency
Agent Interactions and Multi-Agent Ecosystems
- Cooperative and competitive dynamics
- Collective memory and shared state management
- 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
- Architectural trends and research frontiers
Conclusion and Subsequent Steps
Requirements
- A solid grasp of autonomous agent architectures
- Practical experience with large-scale AI systems
- Knowledge of reinforcement learning principles
Target Audience
- Senior AI engineers
- Architects of agent platforms
- Research and Development teams
