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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 artifacts.
- Automated evaluation pipelines.
- Incorporating human judgment into learning loops.
Advanced Optimization and Future Directions
- Performance tuning for long-duration tasks.
- Predictive modeling 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.
- R&D teams.
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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