Money Forward brings Cursor’s coding agents to product, design, and QA

2026-03-18 · Source: Cursor Blog · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

Money Forward, a financial services company in the Asia-Pacific region, has successfully integrated Cursor's coding agents across its engineering, product, design, and quality assurance (QA) teams, with over 1,000 employees now using the tool daily. Initially, engineering saw developers save 15–20 hours per week on tasks like refactoring iOS apps, optimizing Rails, managing AWS/GCP deployments with Terraform, and migrating front-end services. This led to a 30% increase in engineering adoption within a week. The company's Engineering Productivity and AI Research (MEPAR) department selected Cursor for its model-agnostic infrastructure, minimal setup, visual capabilities, unified agent workspace, and reliable performance on large codebases. QA engineers now generate test cases 70% faster, product managers refine requirements by analyzing production code, and designers prototype directly against live frontends, accessing product analytics to inform their work.

Key takeaway

For CTOs and engineering leaders evaluating AI coding tools, you should consider solutions that offer broad applicability beyond core development. Prioritize platforms with robust visual interfaces and unified workspaces to drive adoption across product, design, and QA teams, not just engineers. This approach can yield significant time savings and improve overall software development lifecycle efficiency by enabling cross-functional teams to interact directly with code and data.

Key insights

AI coding agents can significantly boost productivity across an entire software development lifecycle, not just engineering.

Principles

• Agent-based tools improve adoption over basic AI chat.
• Visual interfaces enhance non-developer AI tool acceptance.

Method

Money Forward implemented Cursor by first proving value in engineering, then expanding to product, design, and QA, leveraging its model-agnostic infrastructure and visual capabilities for broader adoption.

In practice

• Use agents for end-to-end software engineering tasks.
• Automate test case generation with AI agents.
• Enable designers to prototype directly in code.

Topics

• AI Coding Agents
• Software Development Lifecycle
• Test Automation
• Product Management
• Financial Services Technology

Best for: CTO, Executive, Investor, AI Engineer, Software Engineer, Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Cursor Blog.