Building

I build infrastructure and tools for agentic AI systems, with focus on reliability, scalability, and practical deployment in production environments.

Current focus

My engineering work centers on the infrastructure required for reliable agentic AI systems: orchestration frameworks, context management, and human-AI collaboration interfaces.

→ Multi-agent coordination and task delegation
→ LLM context and memory management systems
→ Human-in-the-loop governance mechanisms
→ Monitoring and observability for autonomous workflows

Approach

Problem-driven development. Engineering decisions follow from requirements, not technology preferences. I prioritize solutions that address real operational needs.

Production-first validation. Systems are validated through deployment. I iterate based on real-world performance rather than theoretical benchmarks alone.

Open source by default. I release tools and frameworks openly when they may benefit the broader research and engineering community.

AI safety considerations

Safety is a design constraint, not an afterthought. My systems incorporate human oversight by default, graceful degradation under failure conditions, and explicit boundaries on autonomous decision-making. I believe reliable AI requires transparent limitations and appropriate human governance.

Open source

Selected tools and frameworks available on GitHub.