SuperOptiX AI

After 6 years working in the Developer Experience team at FAANG, I took a giant leap of faith to build what I believe is the next leap in AI infrastructure. Why? Because Agentic AI today is fragmented, untestable, and too often just “prompt glue.” Building reliable, scalable AI agents for production has been one of the most significant challenges in the AI industry. While countless frameworks excel at creating impressive demos, they consistently fail when faced with real-world conditions. The industry has been plagued by fragile prompts, missing evaluation layers, inadequate optimization workflows, poor multi-agent support, and a lack of proper observability tools. You probably have seen brilliant AI projects fail in production because they lacked the foundational infrastructure needed for real-world deployment. No wonder why AI projects are failing to get into the production.

SuperOptiX was born from this frustration and the determination to solve these problems. It's the result lot of R&D, obsession, and late-night coding. A framework that treats agent design like engineering, where evaluation comes before orchestration, where optimization is not optional — it’s core.
SuperOptiX is Full-Stack Agentic AI Framework designed for Context and Agent Engineering. Unlike most frameworks that bolt on evals, optimization and monitoring as an afterthought, SuperOptiX makes evaluation, optimization, and guardrails core to the development lifecycle. Thanks to DSPy's evaluation-first philosophy and the BDD/TDD concepts form software engineering that empower launch of SuperOptiX.
If you're building AI agents, copilots, or full workflows or multi-agentic systems — I’d love for you to try SuperOptiX. If you’re building AI agents, copilots, or complex workflows, I’d love for you to try SuperOptiX and share your feedback.