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Course Outline
Comprehending Antigravity’s Agent Architecture
- Internal representations and state models
- Coordinated layered behavior
- Action generation pathways
Memory Systems for Long-Lived Agents
- Behavioral differences between short-term and long-term memory
- 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
- Techniques for self-evaluation and self-correction
Learning Over Time
- Monitoring agent learning progress
- Identifying and mitigating skill decay
- Adaptive updates based on operational context
Knowledge Base Construction and Retention
- Creating structured long-term knowledge graphs
- Semantic retrieval and memory indexing
- Ensuring knowledge relevance and freshness
Agent Interactions and Multi-Agent Ecosystems
- Cooperative and competitive behaviors
- Collective memory and shared state management
- Scaling emergent patterns across systems
Developer Feedback Integration
- Reviewing and annotating agent artifacts
- Automated evaluation pipelines
- Integrating human judgment into learning loops
Advanced Optimization and Future Directions
- Performance tuning for long-duration tasks
- Predictive modeling of agent evolution
- Architectural trends and research frontiers
Conclusion and Next Steps
Requirements
- A solid understanding of autonomous agent architectures
- Experience working with large-scale AI systems
- Familiarity with reinforcement learning principles
Audience
- Senior AI engineers
- Agent platform architects
- R&D teams
14 Hours
