Bringing AI agents into the enterprise software development lifecycle is fast becoming the norm. As developers experiment with new platforms, organizations are exposed to potential security and orchestration failures. Systems that work in pilots may fail once the agents start working with real-time data.Legacy tech giant IBM is one of several companies trying to address that gap by introducing more structure into how these workflows run. Yesterday, it announced the global launch of its AI-powered software development platform Bob, designed to write and test code across the development cycle, already in use by more than 80,000 of its employees after starting with just 100 internal users in summer 2025. Bob introduces a structured layer that constantly pauses for human-led checkpoints, yet by harnessing AI models to perform agentic tasks, IBM says it has saved some teams up to 70% of time “on selected tasks…equaling an average time savings of 10 hours per week.”Specific models supported include IBM’s own Granite series, Anthropic’s Claude, some from French AI firm Mistral and other smaller distilled models — no Alibaba Qwen or other fully open source ones. This approach reflects a shift in how enterprises want to approach AI-led development: to build systems that not only build applications but also execute complex, multi-step workflows that do not rely on a single model or a single orchestration framework. It provides a structured, guarded approach to automation that seeks to center humans more in the process and fill audit gaps. Neal Sundaresan, general manager, Automation and AI at IBM, told VentureBeat in an exclusive interview that a large part of using AI for software development is being systematic. “Model capability alone isn’t enough,” Sundaresan said. “How you deploy it, how you structure context, and how you keep humans in the loop is what determines whether AI actually delivers.”That …
