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Can A Chatbot Really Fix A Mainframe?
Rocket Software launches an enterprise virtual assistant to bridge the widening skills gap in mission-critical IT environments.
01/28/2026
Key Highlights
- The new assistant aims to deliver natural language diagnostics for complex mainframe and hybrid cloud environments.
- By using the industry standard Model Context Protocol, the tool integrates with existing large language models while maintaining data governance.
- The solution aims to reduce mean time to resolution by tracing system symptoms directly to the responsible lines of code.
- The assistant is architected to lower the barrier to entry for junior developers who lack deep legacy systems expertise.
The News
Rocket Software has officially launched Rocket EVA, an enterprise virtual assistant designed to automate operational diagnostics for core systems. Built on the Rocket MCP Server, the tool enables teams to query mainframes and distributed environments using conversational language. It seeks to provide a unified path to system insights without requiring users to navigate multiple complex diagnostic tools. Find out more by clicking here to read the press release.
Analyst Take
We have long observed that the most significant bottleneck in the modernization of mission-critical systems is not the hardware itself, but the pool of human expertise required to manage it. As the veteran guard of mainframe systems engineers approaches retirement, organizations find themselves in a tough spot; they are tethered to reliable, high-performance systems that few people left in the building truly understand. Rocket Software’s introduction of Rocket EVA is a calculated attempt to solve this demographic concern through the application of conversational intelligence.
In our view, this is not merely another chatbot implementation. While the broader market is currently saturated with generic productivity assistants, the challenge in the mainframe space is significantly more nuanced. Core systems data is famously dense and often siloed within proprietary monitoring tools. By introducing an assistant specifically architected for these environments, we see Rocket Software attempting to democratize the deep technical knowledge once held exclusively by systems programmers. This shift allows a generalist developer or a junior operator to ask a simple question in plain language and receive a diagnostic response that previously would have required hours of manual log correlation and specialized command-line expertise.
What Was Announced
The launch centers on Rocket EVA, an enterprise virtual assistant that functions as a conversational interface for IT operations. The tool is powered by the Rocket MCP Server, which uses the Model Context Protocol to provide governed access to system data. This architecture allows the assistant to connect to various large language models while ensuring that sensitive core system data remains protected. The assistant includes an Analyzer component designed to correlate symptoms across the tech stack and a Recommender engine that aims to suggest specific remediation steps.
Technical specifications highlight the use of Rocket’s existing data scanners to provide deep visibility into system interactions. The solution is architected to be hybrid-ready, meaning it is designed to scale across mainframe, distributed, and cloud environments. It aims to deliver end-to-end diagnostics by tracing an issue from the initial user-reported symptom through the system layers to the exact line of code causing the friction. Furthermore, the platform is designed to be open, allowing for integration with third-party MCP-compliant tools to broaden the diagnostic scope beyond Rocket’s own ecosystem.
We see the decision to utilize the Model Context Protocol as a particularly astute move. By adopting an open standard, Rocket is avoiding the trap of a closed-loop system. The adoption of open source is on brand for Rocket Software. This allows enterprises to leverage their existing investments in AI models while using Rocket as the secure conduit to the "black box" of the mainframe. We have often seen vendors try to force customers into proprietary AI stacks, which usually results in slow adoption. This more flexible approach aims to deliver a faster path to value by fitting into the customer's existing AI strategy rather than forcing a new one.
However, the efficacy of such a tool hinges entirely on the quality of the underlying data and the accuracy of the domain-specific training. Mainframe environments are notoriously noisy. For Rocket EVA to gain widespread traction, it must prove that its Recommender engine can consistently provide actionable advice that does not lead to unintended consequences in production. We are encouraged by the focus on non-disruptive diagnostics, as any tool that adds significant overhead to a busy mainframe is usually discarded quickly by the operations team.
When looking at the broader competitive landscape, this move puts Rocket in a distinct position compared to traditional rivals. While companies like IBM and BMC have their own AIOps and assistant initiatives, Rocket's focus on a protocol-based, "hero component" for modernization suggests they want to be the universal translation layer for the hybrid enterprise. The goal here is clearly to turn the mainframe into just another node in a modern DevOps toolchain. We see this as a pragmatic response to the reality that most firms are not looking to leave the mainframe, but they are desperate to make it easier to live with.
Looking Ahead
Based on what we are observing, the launch of Rocket EVA represents a significant pivot toward what we call the "conversational infrastructure" era. The key trend that we are going to be looking out for is the actual rate of adoption among the skeptical, old-school systems administration community. Our perspective is that while the technology is impressive, the cultural shift required to trust an AI-driven assistant with core system diagnostics is substantial. Going forward, we are going to be closely monitoring how the company performs on the integration of third-party MCP tools, as the true power of this platform lies in its ability to act as a central nervous system for heterogeneous environments.
When you look at the market as a whole, the announcement reflects a broader move away from "lift and shift" modernization toward a more surgical, intelligence-led approach. We see Rocket EVA as a prime example of this "intelligence-first" strategy. HyperFRAME will be tracking how the company does with customer traction on the vector of measurable reduction in mean time to resolution for its early adopters. If Rocket can prove a significant ROI through reduced downtime and lower training costs, they may well set the standard for the next decade of core systems management. The market is moving fast, and the ability to turn legacy complexity into conversational clarity is no longer a luxury. It is a survival requirement.
Steven Dickens | CEO HyperFRAME Research
Regarded as a luminary at the intersection of technology and business transformation, Steven Dickens is the CEO and Principal Analyst at HyperFRAME Research.
Ranked consistently among the Top 10 Analysts by AR Insights and a contributor to Forbes, Steven's expert perspectives are sought after by tier one media outlets such as The Wall Street Journal and CNBC, and he is a regular on TV networks including the Schwab Network and Bloomberg.