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 models
- Coordination of layered behaviors
- Pathways for action generation
Memory Systems for Long-Lived Agents
- Distinguishing short-term from 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 for self-evaluation and self-correction
Learning Over Time
- Monitoring agent learning progress
- Identifying and mitigating skill decay
- Adaptive updates driven by operational context
Constructing and Retaining Knowledge Bases
- Developing structured long-term knowledge graphs
- Semantic retrieval and memory indexing
- Maintaining the relevance and freshness of knowledge
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
- Incorporating 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 understanding of autonomous agent architectures
- Hands-on experience with large-scale AI systems
- Proficiency in reinforcement learning concepts
Target Audience
- Senior AI engineers
- Architects of agent platforms
- R&D teams
