Khwand - Test, harden, and monitor AI agents

Hey Product Hunt 👋 Khwand started from a frustration we kept seeing everywhere in AI development: teams can build AI products incredibly fast — but keeping them reliable in production is still painfully hard. You can vibe-code a feature in an afternoon, ship an agent workflow, or wire up a powerful LLM app quickly. But the moment prompts drift, models update, edge cases appear, or user behavior changes, things quietly break. Traditional CI/CD wasn’t designed for software where the logic itself is probabilistic. We built Khwand because we wanted an answer to a simple question: **How do you ship AI-native software with confidence?** At first, we thought the solution was “better testing.” But while building, our thinking evolved. We realized static tests weren’t enough for systems powered by LLMs. AI apps need something closer to an autonomous quality layer — one that stress-tests changes, catches regressions, benchmarks model behavior, and fixes failures *before* users experience them. That shift completely changed our approach and became the foundation of Khwand. Today, Khwand acts like a self-healing CI/CD layer for AI-native software — helping teams test, validate, and stabilize LLM-powered apps and agentic workflows without relying on guesswork. This launch is exciting because we know we’re not the only ones wrestling with brittle AI systems and unpredictable behavior. If you’re building with LLMs, agents, or vibe-coded products, we’d genuinely love to hear: **what’s been your biggest reliability headache so far?** 👀