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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 versus 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 calibration
- Techniques for self-evaluation and self-correction
Continuous Learning
- 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 methods
- Ensuring the relevance and currency of knowledge
Agent Interactions and Multi-Agent Ecosystems
- Cooperative and competitive behavioral patterns
- Collective memory and shared state management
- Scaling emergent patterns across distributed systems
Developer Feedback Integration
- Reviewing and annotating agent-generated artifacts
- Implementing automated evaluation pipelines
- Weaving 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
- A solid grasp of autonomous agent architectures
- Hands-on experience with large-scale AI systems
- Knowledge of reinforcement learning principles
Target Audience
- Senior AI engineers
- Architects specializing in agent platforms
- R&D teams
