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Duration 14 hours
Course Outline
Exploring 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 adjustment techniques.
- Methods for self-evaluation and self-correction.
Learning Over Time
- 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 knowledge relevance and freshness.
Agent Interactions and Multi-Agent Ecosystems
- Navigating cooperative and competitive behaviors.
- Managing collective memory and shared state.
- Scaling emergent patterns across systems.
Developer Feedback Integration
- Reviewing and annotating agent artifacts.
- Implementing automated evaluation pipelines.
- Weaving human judgment into learning loops.
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 of agent platforms.
- R&D teams.
