AI agents and automation systems for building, managing, and optimizing training data pipelines for modern AI models.
This repository focuses on execution systems — turning AI training data into scalable, automated infrastructure.
- Dataset generation agents
- RLHF automation agents
- Data labeling orchestration
- QA and validation agents
- Evaluation + benchmarking systems
- Continuous optimization loops
AI is shifting from:
❌ manual workflows
➡️ autonomous data systems
Agents are the layer that:
- reduce cost
- increase speed
- improve data quality
- enable scale
Automate:
- synthetic data creation
- multimodal dataset generation
- edge case expansion
Automate:
- feedback collection
- ranking systems
- preference modeling pipelines
Ensure:
- data accuracy
- consistency
- quality scoring
Run:
- benchmarks
- performance scoring
- regression testing
Rhonda Coleman Albazie
Founder • Operator • CTO
AI-Native | Robotics-Native | Cloud-Native | Cyber-Native | Physics-Native