Today, many software teams are adding AI features into their apps — like customer support bots, writing tools, or internal copilots — by writing “prompts” directly into their code. These prompts tell the AI what to say or do. But once the product is live, there's no visibility into what the AI is actually saying to users, how much it’s costing, or when things silently go wrong — like hallucinations, tone drift, or token overuse. I’m hoping to build a solution that helps teams keep these AI features healthy and reliable in production. They’ll have a central database to store all their prompts, test different versions across multiple AI models, compare costs and outputs, and — most importantly — evaluate the “human touch” of the responses. The platform would enable A/B testing across prompt versions to identify which responses perform best — whether in terms of marketing impact, sales conversion, engagement, or overall usage. It would track every AI response, detect unusual or risky behavior, and suggest — or even apply — fixes automatically. Think of it as a real-time quality control system for the AI layer of your product. The system would be powered by lightweight autonomous agents that watch every model call, flag anomalies, and make context-aware recommendations — or take direct action when safe to do so. These agents would monitor prompt behavior over time, compare version performance, and optimize for clarity, safety, and cost. Technically, it’s a real-time observability and correction runtime — like Datadog + LaunchDarkly, but built specifically for managing AI prompts and agentic behavior in production.