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Duration 14 hours
Course Outline
Grasping Antigravity’s Agent Architecture
- Internal representations and state models
- Coordinated behavior across layers
- Pathways for action generation
Memory Systems for Long-Lived Agents
- Contrasting short-term and long-term memory behaviors
- Patterns for persistent knowledge storage
- Safeguarding against memory corruption and drift
Feedback Loops and Behavioral Shaping
- Human-in-the-loop feedback strategies
- Reinforcement mechanisms and reward tuning
- Techniques for self-evaluation and correction
Temporal Learning Dynamics
- Monitoring agent learning progression
- Identifying and mitigating skill decay
- Context-aware adaptive updates
Constructing and Retaining Knowledge Bases
- Developing structured long-term knowledge graphs
- Semantic retrieval and memory indexing
- Ensuring knowledge relevance and freshness
Agent Interactions and Multi-Agent Ecosystems
- Cooperative versus competitive behaviors
- Collective memory and shared states
- Scaling emergent patterns across systems
Integrating Developer Feedback
- Annotating and reviewing agent artifacts
- Automated evaluation pipelines
- Embedding human judgment into learning cycles
Advanced Optimization and Future Trajectories
- Tuning performance for long-duration tasks
- Predictive modeling of agent evolution
- Architectural trends and research frontiers
Conclusion and Next Steps
Requirements
- A solid grasp of autonomous agent architectures.
- Practical experience with large-scale AI systems.
- Proficiency with reinforcement learning principles.
Target Audience
- Senior AI Engineers.
- Agent-Platform Architects.
- R&D Teams.
