Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Exploring Antigravity’s Agent Architecture
- Internal representations and state modeling
- Coordination of layered behaviors
- Pathways for action generation
Memory Systems for Long-Lived Agents
- Differentiating 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 tuning
- Techniques for self-evaluation and self-correction
Learning Over Time
- Monitoring agent learning progress
- Identifying and addressing skill decay
- Context-based adaptive updating
Knowledge Base Construction and Retention
- Constructing structured long-term knowledge graphs
- Semantic retrieval and memory indexing
- Ensuring knowledge relevance and currency
Agent Interactions and Multi-Agent Ecosystems
- Cooperative versus competitive dynamics
- Collective memory and shared states
- Scaling emergent patterns across systems
Developer Feedback Integration
- Reviewing and annotating agent outputs
- Automated evaluation pipelines
- Weaving human judgment into learning cycles
Advanced Optimization and Future Directions
- Performance tuning for extended-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
- Agent-platform architects
- R&D teams
14 Hours
