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 Persistent Agents
- Distinguishing between short-term and long-term memory behaviors
- Patterns for persistent knowledge storage
- Strategies to prevent memory corruption and drift
Feedback Loops and Behavioral Shaping
- Human-in-the-loop feedback methodologies
- Reinforcement mechanisms and reward calibration
- Techniques for self-evaluation and self-correction
Temporal Learning Dynamics
- Metricizing agent learning progress
- Identifying and mitigating skill degradation
- Adaptive updates driven by operational context
Building and Maintaining Knowledge Bases
- Constructing structured long-term knowledge graphs
- Semantic retrieval and memory indexing strategies
- Ensuring ongoing knowledge relevance and freshness
Agent Interactions and Multi-Agent Ecosystems
- Cooperative versus competitive behavioral dynamics
- Shared state and collective memory mechanisms
- Scaling emergent patterns across distributed systems
Integrating Developer Feedback
- Reviewing and annotating agent-generated artifacts
- Automated evaluation pipelines
- Weaving human judgment into learning cycles
Advanced Optimization and Future Trajectories
- Performance tuning for extended-duration tasks
- Predictive modeling of agent evolution
- Architectural trends and research frontiers
Summary and Recommended Next Steps
Requirements
- A solid grasp of autonomous agent architectures
- Hands-on experience with large-scale AI systems
- Proficiency in reinforcement learning concepts
Intended Audience
- Senior AI engineers
- Agent platform architects
- R&D teams
14 Hours
