This quarter marks another milestone in our journey to help engineering teams reduce friction, elevate quality, and accelerate delivery at scale. With IBM Engineering AI Hub 1.2, we are expanding the AI-powered capabilities inside the engineering lifecycle with the new Work Item compose agent, so teams can spend less time on manual, repetitive work and more time building the products and systems that matter. Together with the new web-based client for simplified administration and configuration, this release helps organizations move faster with greater quality, consistency, and control.

Here’s what’s new in this release to help teams work more efficiently with Engineering AI Hub:

Work Item compose agent

The new Work Item compose agent generates high quality‑ drafts for epics, features, and user stories directly in IBM Engineering Workflow Management. It helps teams reduce the time they spend clarifying intent and improves overall planning quality. This capability enables practitioners to start with a clear foundation, which improves estimation accuracy and reduces downstream rework.

For more information, see Work Item compose agent.

Requirements quality analysis: expanded use cases

Teams can now dismiss quality scores for requirements that follow established writing patterns or domain-specific‑ formats. This improvement ensures that review cycles focus on requirements that need attention instead of those that already meet organizational standards.

For more information, see Analyzing requirement quality.

Admin console for Engineering AI Hub

Version 1.2 introduces a dedicated admin console that provides a streamlined interface to configure and manage all AI Hub agents. Administrators can also view daily license usage, which helps improve tracking and reduces configuration errors.

For more information, see Administering Engineering AI Hub.

Configurable category thresholds for requirements quality analysis

Administrators can now customize the threshold values for Low, Mid, and High score categories in the Requirements quality analysis agent. This allows teams to adapt scoring to internal expectations, resulting in more consistent evaluations and higher confidence in quality reports.

For more information, see Analyzing requirement quality.

Custom quick starters for the Engineering Assistant

The Engineering Assistant now supports customized quick start prompts. Administrators can update default quick start‑ queries or create new sets that match team workflows. This flexibility improves adoption and increases the relevance of generated insights for each engineering function.

For more information, see Configuring the Engineering Assistant agent.

Support for watsonx.ai on prem‑ deployments

Organizations can now configure IBM Engineering AI Hub to use watsonx.ai deployed on premises to maintain greater control over data, compliance, and integration. This option helps teams meet data and regulatory requirements while still benefiting from the full set of IBM Engineering AI Hub capabilities.

Private Preview: Extending AI across workflows and developer experiences

As AI adoption matures, teams are looking to extend AI automation beyond individual tasks into connected workflows and developer tools. This release of IBM Engineering AI Hub brings this vision closer with a private preview of natural‑language access to ELM data via MCP tools, enabling seamless interaction with engineering data across IDEs, assistants, and chat interfaces. The preview, also introduces creation of agentic AI workflows that let Engineering AI Hub agents and custom agents collaborate in orchestrated, multi‑step automations.

In Summary

IBM Engineering AI Hub 1.2 builds on our commitment to deliver AI capabilities that fit naturally into engineering workflows. These enhancements help teams plan with more confidence, elevate the quality of requirements, and streamline administrative tasks at scale.

If you haven’t explored IBM Engineering AI Hub yet, now is a great time to dive in, explore these updates, try the existing and new agents in your daily workflows, and share your feedback with the us so we can continue improving the engineering experience together. For the private preview, contact IBM Engineering Product Management.

Bhawana Gupta
Senior Product Manager, IBM Engineering