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Duration 14 hours
Course Outline
Examining Antigravity’s Agent Architecture
- Internal representations and state modeling
- Coordination of layered behaviors
- Pathways for action generation
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 methodologies
- Reinforcement mechanisms and reward tuning
- Techniques for self-evaluation and self-correction
Temporal Learning Processes
- Monitoring agent learning progression
- Identifying and mitigating skill decay
- Context-based adaptive updates
Knowledge Base Construction and Retention
- Developing structured long-term knowledge graphs
- Semantic retrieval and memory indexing strategies
- Ensuring knowledge relevance and freshness
Agent Interactions and Multi-Agent Ecosystems
- Cooperative versus competitive dynamics
- Shared state and collective memory
- Scaling emergent patterns across systems
Integrating Developer Feedback
- Reviewing and annotating agent outputs
- Automated evaluation workflows
- Weaving human judgment into learning cycles
Advanced Optimization and Future Prospects
- Tuning performance for long-duration tasks
- Predictive modeling of agent evolution
- Emerging architectural trends and research frontiers
Recap and Action Items
Requirements
- Working knowledge of autonomous agent architectures
- Hands-on experience with large-scale AI systems
- Proficiency in reinforcement learning principles
Target Audience
- Senior AI engineers
- Architects of agent platforms
- R&D teams
