AI-built applications often work in development environments but fail in production due to a fundamental gap: AI builders optimize for iteration, not real infrastructure demands. This leads to problems like vendor lock-in, lack of deployment history, and no rollback capabilities when trying to go live.
A user is experiencing production issues with multi-agent systems and has developed a basic chaos monkey framework for agents. They are looking to collaborate with domain experts to enhance the tool, use it for benchmarking, and improve customer experience.
This article discusses the problem of AI-built applications working in development but failing in production. It highlights challenges such as proprietary databases, lack of deployment history, and no rollback mechanisms, leading to issues with infrastructure ownership and vendor lock-in.
This article recounts the breakdown of an LLM-powered R data analytics pipeline that performed well in a proof-of-concept but utterly failed at scale. The story aims to warn and educate about the challenges of integrating large language models into R data workflows in production.
AI-built applications often fail at scale not due to the AI's fault, but due to incorrect expectations about the underlying infrastructure. Deploying an AI-built app means inheriting infrastructure decisions optimized for iteration speed, not load handling, leading to issues like connection timeouts and escalating database costs.
The article discusses the challenge of scaling AI-built applications, which are optimized for rapid iteration but fail in production due to a lack of control over the underlying infrastructure. It highlights the transition from fast prototypes to robust production solutions, where the invisibility of database tiers and optimization becomes a critical bottleneck.
This article discusses why non-coding AI agents fail in production, referencing an Anthropic write-up and a survey indicating high failure rates. It criticizes a Reddit thread for oversimplifying the cause to "missing memory," suggesting there are multiple other factors and proposing five fixes. These fixes are presented as cost-effective solutions for common deployment challenges.