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Duration 14 hours
Course Outline
Understanding Antigravity’s Agent Architecture
- Internal representations and state models
- Layered behaviour coordination
- Action generation pathways
Memory Systems for Long-Lived Agents
- Short-term vs long-term memory behaviours
- Persistent knowledge storage patterns
- Preventing memory corruption and drift
Feedback Loops and Behaviour Shaping
- Human-in-the-loop feedback strategies
- Reinforcement mechanisms and reward adjustment
- Self-evaluation and self-correction techniques
Learning Over Time
- Tracking agent learning progress
- Detecting and mitigating skill decay
- Adaptive updating based on operational context
Knowledge Base Construction and Retention
- Building structured long-term knowledge graphs
- Semantic retrieval and memory indexing
- Maintaining knowledge relevance and freshness
Agent Interactions and Multi-Agent Ecosystems
- Cooperative and competitive behaviours
- Collective memory and shared state
- Scaling emergent patterns across systems
Developer Feedback Integration
- Reviewing and annotating agent artefacts
- Automated evaluation pipelines
- Incorporating human judgement into learning loops
Advanced Optimization and Future Directions
- Performance tuning for long-duration tasks
- Predictive modelling of agent evolution
- Architectural trends and research frontiers
Summary and Next Steps
Requirements
- A solid understanding of autonomous agent architectures
- Practical experience with large-scale AI systems
- Familiarity with reinforcement learning concepts
Target Audience
- Senior AI engineers
- Agent-platform architects
- R&D teams
