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
Understanding Antigravity’s Agent Architecture
- Internal representations and state models.
- Layered behaviour coordination.
- Action generation pathways.
Memory Systems for Long-Lived Agents
- Short-term versus 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 judgment 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.
- Experience working with large-scale AI systems.
- Familiarity with reinforcement learning concepts.
Target Audience
- Senior AI engineers.
- Agent-platform architects.
- Research and development (R&D) teams.
14 Hours
