Our client is a leading global management consulting firm that helps organizations drive strategy, transformation, and business growth. Their QA teams spent up to 18 hours per sprint on manual test design, documentation, and traceability activities, making it hard to maintain consistent test coverage and focus on higher-value quality improvements.

They wanted to leverage AI to fast-track the testing processes. Nagarro helped them transform the way business requirements were translated into test cases and quality checks. Using the MCP protocol, we integrated Claude, Jira, and QMetry into a governed, AI-powered workflow that automates key QA activities, strengthens traceability, enforces review controls, and improves release confidence.

the challenge

The client's QA team had to create comprehensive test coverage for every medium to complex Jira story, upload test assets into QMetry, and manually maintain links between requirements and test suites. These activities consumed 12-18 hours of QA effort per sprint for a typical workload of five to six medium to complex user stories, diverting skilled engineers from higher-value quality engineering activities.

The manual process also made it difficult to maintain consistent test coverage and end-to-end traceability across systems. With testing activities spread across multiple tools, aligning requirements with test assets required significant effort, increasing the risk of inconsistencies and limiting the team's ability to scale efficiently.

 

A network globe with a Nagarro scribble and design

the solution

We designed and deployed an end-to-end AI-powered automation pipeline connecting three enterprise platforms: Claude (Anthropic), Jira (Atlassian), and QMetry (SmartBear) through MCP (Model Context Protocol) connectors. This integration eliminated the manual test case creation and upload effort altogether, while also establishing full requirement-to-defect traceability and on-demand status visibility on Jira.

Beyond test automation, the solution creates end-to-end traceability across requirements, test cases, and defects, improving visibility and governance throughout the QA lifecycle. It also provides real-time project updates by automatically retrieving the latest information and posting it to relevant tickets, reducing manual effort and keeping stakeholders aligned.

Individual Agents (1)

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Jira Claude integration

Connected Claude, Jira, and QMetry through MCP to automate test case management and maintain end-to-end traceability from requirements to testing. QA only needs to mark a story as QA Ready to start the workflow.

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AI-driven test design

Configured Claude to automatically generate comprehensive test cases covering positive, negative, edge, boundary, and non-functional scenarios, helping standardize test coverage across user stories.

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Built-in quality controls

Introduced a mandatory QA review step in Jira to ensure all AI-generated test cases are validated before being approved for execution and release.

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Automated test management 

Automated the creation and upload of approved test cases to QMetry, along with test suite setup and sprint-level organization, reducing manual effort and streamlining test administration.

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End-to-end traceability 

Established clear links between requirements, test cases, and defects, providing a connected view of the testing lifecycle and simplifying audit and release-readiness activities.

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Real-time status updates 

Enabled teams to retrieve the latest testing and execution status through simple prompts, with updates automatically posted to Jira, reducing manual follow-ups and improving visibility.

the impact

The solution significantly reduced the time and effort required for test case preparation, allowing QA engineers to focus on exploratory testing and other high-value quality engineering activities.

It also improved consistency, traceability, and scalability across the testing lifecycle. 83% reduction in test preparation effort, with test case creation time reduced from 2.5-3 hours to approximately 0.5 hour per medium to complex user story. For a sprint with 5-6 such stories, preparation effort dropped from 12.5-18 hours to 2.5-3 hours.

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83% reduction in test preparation effort, with test case creation time reduced from 2.5-3 hours to approximately 0.5 hours per medium- to complex user story. For a sprint with 5-6 such stories, preparation effort dropped from 12.5-18 hours to 2.5-3 hours.

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Greater QA capacity, freeing 11-14 hours per sprint and 274-328 hours annually per engineer, enabling teams to take on additional work or invest more time in exploratory testing.

  

More predictable, auditable, and scalable quality assurance, with consistent test coverage across all stories, mandatory review-driven validation, and end-to-end Jira-QMetry traceability that eliminates manual cross-referencing and supports seamless scaling across teams and sprints.