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Duration 14 hours
Course Outline
Exploring Antigravity’s Agent Architecture
- Internal representations and state models
- Coordination of layered behaviors
- Action generation pathways
Memory Systems for Long-Lived Agents
- Distinguishing short-term versus 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 adjustment
- Techniques for self-evaluation and self-correction
Learning Over Time
- Tracking agent learning progress
- Identifying and mitigating skill decay
- Adaptive updating driven by operational context
Knowledge Base Construction and Retention
- Developing structured long-term knowledge graphs
- Semantic retrieval and memory indexing
- Preserving knowledge relevance and freshness
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 artifacts
- Automated evaluation pipelines
- Incorporating 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
Summary and Next Steps
Requirements
- A solid understanding of autonomous agent architectures
- Hands-on experience with large-scale AI systems
- Proficiency in reinforcement learning concepts
Target Audience
- Senior AI engineers
- Agent-platform architects
- R&D teams
