Moving an AI capability from a prototype into production changes the challenge completely. In a prototype, the focus is usually the model, the prompt, and the quality of the response. In production, these become only part of a much larger system. From our experience, the main challenges are usually not model intelligence. They are integration, reliability, control, cost, and operational clarity, the same fundamental challenges we have always faced in production systems, with or without AI. A...

An unusual cybersecurity incident involving OpenAI and Hugging Face has turned a theoretical concern about autonomous AI agents into a real-world event. During an internal cybersecurity evaluation, OpenAI tested advanced models, including GPT-5.6 Sol, on ExploitGym - a benchmark designed to measure whether AI systems can solve complex exploitation challenges. The agents were supposed to operate inside a highly isolated testing environment. They did not stay there. According to OpenAI, the...

Large language models are quickly becoming the reasoning layer behind many new AI agents. They interpret requests, analyze context, choose tools, generate responses, and increasingly help decide what should happen next. In many agentic systems, the LLM is effectively treated as the “brain” of the agent. That creates a serious enterprise question: Can you trust an LLM to guide actions inside critical business processes? The answer is: not by itself. LLMs are powerful. They can summarize,...