High-trust engineering domains and AI Agents: Introducing IBM Engineering AI Hub v1.0 (available this month)

Engineering teams are undergoing far-reaching digital transformation in complex engineering and product lifecycle management, and generative AI can help. However, in engineering domains where accuracy and compliance are non-negotiable, and results must be correct, applying generative AI cannot be done naïvely.

Even a single wayward requirement can lead to costly rework, schedule delays and low-quality outcomes that can risk product and sometimes company viability. This is especially vital for safety-critical, regulated industries such as automotive, aerospace, or medical devices, where mistakes can impact product and business success. AI applications that lack enterprise-grade rigor or skip critical safeguards can undermine the promise of speed and automation they aim to deliver.   

Doing it right can lead to greater speed, clarity and build confidence for your engineering teams and stakeholders.

One promising pattern for AI in engineering is to apply task-level AI automations that are powered by purpose-built agents designed specifically to fulfill these tasks. This enables new kinds of automations while addressing some ofthe risk of errors arising from general queries to large language models (LLMs).

We’ve taken this approach in the upcoming IBM Engineering AI Hub v1.0 product. It’s an add-on to IBM Engineering Lifecycle Management, which will provide agents that appear to users as new “smart” features in their existing tools.

The first release, with planned availability starting 14 October 2025, includes agents designed for these use cases:

• Requirements quality analysis: Efficiently write high-quality requirements. Use agent-generated quality scores to address hidden risks among your requirements specifications.
• Ask your requirements: Interact with your requirements using natural language to get answers to your questions, summaries, translations and more.
• Work item synopsis: Quickly switch context, getting up to speed on long-running, complex tasks, defects and other work items.

Engineers will be able to access these AI-powered results in an integrated user experience when using the requirements engineering application DOORS Next and tracking and planning application Workflow Management.

Agents are promising, but they are not a panacea. Our design principles include keeping humans in the driver’s seat (think: AI recommends, human decides), setting realistic expectations with users and gathering evidence through verification and validation that task-level automations demonstrate a sufficient level of accuracy, reliability and usefulness.

As products and systems become increasingly “software defined” and as complexity grows, the need for a smarter, more automated tools  has never been greater. IBM Engineering AI Hub is built to be a step in this direction.

Learn more about IBM Engineering AI Hub

Talk to an expert today to explore how you can get ready for when it becomes available this month