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.
Duration 14 hours
Course Outline
Exploring Antigravity's Agent Architecture
- Internal representations and state modeling
- Coordinated behavior across layers
- Pathways for action generation
Memory Systems for Long-Lived Agents
- Distinguishing short-term versus long-term memory dynamics
- Patterns for persistent knowledge storage
- Mitigating memory corruption and drift
Feedback Loops and Behavior Shaping
- Strategies for human-in-the-loop feedback
- Reinforcement mechanisms and reward tuning
- Techniques for self-evaluation and correction
Learning Over Time
- Monitoring agent learning progression
- Identifying and addressing skill decay
- Adaptive updates driven by operational context
Knowledge Base Construction and Retention
- Developing structured long-term knowledge graphs
- Semantic retrieval and memory indexing
- Ensuring 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
Integrating Developer Feedback
- Reviewing and annotating agent outputs
- Automated evaluation pipelines
- Embedding human judgment into learning cycles
Advanced Optimization and Future Directions
- Performance tuning for extended tasks
- Predictive modeling of agent evolution
- Emerging architectural trends and research frontiers
Conclusion and Next Steps
Requirements
- Foundational knowledge of autonomous agent architectures.
- Practical experience with large-scale AI systems.
- Competence in reinforcement learning principles.
Audience
- Senior AI engineers.
- Agent-platform architects.
- R&D teams.
