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EXANTE’s Codey hits 75% success, but humans keep control

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EXANTE’s Codey hits 75% success, but humans keep control

Investment firm EXANTE has spent six months embedding an autonomous AI developer into its engineering teams. Its experience suggests that the real barrier to AI-led software delivery is not model capability but organisational discipline.

According to EXANTE, its head of web applications, Alina, led the initiative as part of a wider AI transformation programme. Her team built Codey, an in-house multi-agent system that takes a task from a Jira ticket through to a working feature with little human intervention.

Since launch, 250 tasks have passed through the system and 75% were completed successfully. Codey now handles 7% of all work in the teams that use it, and typically moves from the start of coding to a deployed feature environment in around 30 minutes.

EXANTE’s journey began with narrower automation, such as AI code review and analysis of the modules affected by code changes. These tools sped up individual stages but stopped short of full-cycle development. Codey was designed to close that gap. It runs on Claude Agent SDK, Codex and OpenCode, and coordinates six specialised agents: a researcher, planner, developer, reviewer, QA tester and verifier. Different models are assigned to different roles, and work flagged by the reviewer or automated checks is sent back for revision.

The firm now uses Codey across projects ranging from internal CRM and back-office systems to client-facing FinTech services, including its client portal, web trading terminal and desktop application. Tasks can arrive through sprint planning, where teams share a backlog with the system, or through Slack, where Codey clarifies requirements, raises a Jira ticket and returns with a merge request and preview link.


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