This week, I rewrote the agent orchestration framework in Go, introducing structured agent state management along with defined skills and tools integrated with the engine. This significantly improved observability and debugging, as the system now uses Prometheus and Grafana along with a dynamic React dashboard to visualize agent execution, parallel node activity, and tool usage in real time. In addition, to improve user interaction beyond speech-to-text input, I developed a virtual avatar using Three.js that maps engine evaluations to animations, providing more intuitive and engaging feedback to the user during gameplay.
During development, I identified limitations in the current embedding strategy for fine-tuning, particularly with Qwen 7B, where dense vectors made it difficult to align outputs with evaluation signals. As a result, I pivoted toward a heuristic-first approach for move explanation generation, incorporating factors such as game phase, pawn structure, center control, and king safety. I also implemented a LoRA fine-tuning pipeline using expert commentary data, but am delaying full training until the heuristic mapping layer is complete.
Throughout this capstone, I learned several new tools and concepts, including agentic frameworks, observability tools like Prometheus and Grafana, and LoRA fine-tuning techniques. I primarily relied on informal learning strategies such as reading Medium articles, technical blogs, and documentation to quickly get up to speed and apply these tools effectively.