Enterprises that handle sensitive or mission‑critical software face a common dilemma: they need powerful AI‑assisted development tools, but they cannot expose source code, build artifacts, or internal data to public cloud services. Security teams demand full control over where inference runs, compliance officers require audit‑able data residency, and platform engineers look for a seamless experience that works inside existing IDEs and CI/CD pipelines without forcing a rewrite of legacy toolchains. Legacy modernization projects—especially Java, IBM i, and IBM Z workloads—add another layer of complexity because agents must understand decades‑old codebases while respecting strict network isolation.
IBM Bob addresses these challenges with a generally available self‑hosted deployment option. Customers run the Bob agent on their own infrastructure—on‑premises data centers, private clouds, sovereign clouds, or fully air‑gapped networks. The platform does not ship a model; instead, organizations bring their own approved model from IBM’s supported list, such as NVIDIA Nemotron or Poolside Laguna, guaranteeing that all inference stays inside the customer boundary. For workloads that can tolerate external processing, a hybrid mode lets Bob route specific tasks to approved external models like Claude Sonnet, Gemini, or GPT while keeping the core development experience unchanged.
Bob delivers the full agentic lifecycle: understanding code, planning work, executing changes through IDE integration and BobShell, and validating results within the same governed environment. Optional premium packages extend capabilities to Java modernization, IBM i, and IBM Z, giving teams targeted workflows for long‑lived estates. By separating the model layer from the agent, IBM lets security and platform teams decide where inference runs, developers keep a consistent IDE‑based workflow, and enterprises achieve AI sovereignty without sacrificing productivity.
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