As AI adoption becomes an integral part of day-to-day engineering, organizations are discovering that the biggest challenge is not access to powerful models, it's creating a trusted foundation that gives AI secure, governed access to the knowledge it needs to deliver value.
IBM Engineering AI Hub 1.3, generally available from June 18, 2026, is designed to help organizations build that trusted foundation.
This release strengthens the enterprise foundation for AI-powered engineering by expanding trusted access to engineering data, improving interoperability across AI ecosystems, and providing new options for adopting AI within existing enterprise environments.
Turning engineering knowledge into AI-ready context
The most significant enhancement in this release is the introduction of a managed Model Context Protocol (MCP) endpoint for Engineering Lifecycle Management (ELM) data and Rhapsody Systems Engineering models.
One of the biggest challenges in enterprise AI is not building the AI itself, it is giving AI secure, governed access to the information it needs to produce meaningful results. Traditionally, every AI assistant or automation initiative has required teams to create and maintain their own custom integrations with enterprise systems. These integrations often duplicate effort, create inconsistent governance models, and become increasingly difficult to maintain over time.
Engineering AI Hub 1.3 addresses this challenge by introducing MCP Tools for ELM, a trusted, reusable layer that gives AI secure, governed access to engineering information and lifecycle context. This foundation enables four broad categories of AI-powered work:
- Discover relevant engineering artifacts and trusted lifecycle context.
- Analyze engineering data and relationships to uncover insights.
- Act by creating or updating engineering artifacts through AI-assisted interactions.
- Automate multi-step engineering processes using agents and reusable AI skills.
Instead of retrieving isolated records, AI can understand how DOORS Next requirements, work items in Engineering Workflow Management (EWM), tests in Engineering Test Management (ETM), models in Rhapsody SE, and other engineering artifacts relate to one another. This richer context enables more informed recommendations, more accurate responses, and more effective automation.
Just as importantly, MCP Tools inherit the governance model organizations already rely on. Existing role-based permissions, access controls, and lifecycle traceability continue to apply to AI interactions, eliminating the need to create and maintain separate security models for AI. The platform also introduces enterprise controls designed to support AI adoption at scale, helping organizations manage security, system stability, and fair resource utilization as more assistants and automated workflows are introduced.
Perhaps the biggest long-term advantage is reusability. Instead of building a new integration for every assistant, copilot, or agent, organizations can establish a single trusted access layer that can be leveraged across multiple AI applications.
The result is not simply another API. MCP Tools transform engineering data into governed, AI-ready context that can power the next generation of engineering assistants, agents, and orchestrated workflows.
Read more about Engineering AI Hub MCP Tools and the following jazz.net articles:
- From Requirements to engineering insights: AI-assisted Requirements Management with Engineering AI Hub 1.3 MCP Tools
- Beyond queries: AI-assisted Work Item Management with Engineering AI Hub 1.3 MCP Tools
- AI-assisted Test Management with Engineering AI Hub 1.3 MCP Tools
Enabling more open and connected AI ecosystems
Engineering AI Hub 1.3 also helps organizations integrate AI into the way they already work.
With A2A-compliant agent extensibility, customers can incorporate IBM-provided agents into their own orchestrated workflows and preferred frameworks. This allows teams to combine IBM-delivered AI capabilities with their existing AI automation strategies, accelerating adoption without forcing architectural change.
New options for enterprise adoption
Successful AI adoption is not only about capabilities, it is also about fitting into enterprise environments.
Engineering AI Hub 1.3 broadens deployment flexibility by supporting customer-approved LLM providers, starting with Amazon Bedrock, allowing organizations to align with their enterprise AI strategy.
The platform now supports deployment on Kubernetes environments beyond Red Hat OpenShift, giving organizations greater flexibility in how they evaluate and deploy Engineering AI Hub. It also introduces air-gap installation support for organizations operating in highly secure or regulated environments.
Adapting AI to your engineering processes
AI becomes more valuable when it reflects the way your teams actually work. This release introduces support for configuring the Work Item Synopsis agent for custom work item types and provides additional prompt customization for the Work Item Compose agent. These enhancements help teams generate more relevant summaries and higher-quality first drafts that align with their established engineering practices.
Continuing the journey
Engineering AI Hub 1.3 is about more than adding new AI features. It is about creating the trusted, governed, and reusable foundation organizations need to scale AI across the engineering lifecycle. By providing AI-ready access to engineering context through MCP, enabling open agent ecosystems, and removing practical adoption barriers, this release helps teams spend less time building integrations and more time realizing the value of AI.
If you haven't explored IBM Engineering AI Hub yet, this is a great time to see what's possible. Try the new MCP tools, incorporate the existing and new into your everyday engineering workflows to explore how Engineering AI hub 1.3 can help accelerate AI-assisted engineering.
Bhawana Gupta
Senior Product Manager, IBM Engineering