Case Study: Coding Agent Rollout Across a 300-Engineer Organization

Fintech

Coding Agent Rollout Across a 300-Engineer Organization

The Challenge: Every Team Using AI Tools Its Own Way

Engineers at a payments company were already using AI coding tools, each in their own way, and results varied widely between teams. Security was concerned about source code and credentials leaving controlled environments, particularly in services covered by the firm's payment card compliance obligations. Leadership had no measure of whether the tools were helping and no agreed standard for reviewing and testing AI-generated code before release. The firm wanted the productivity gain across the whole engineering organization while keeping the controls its customers and auditors relied on.

Our Solution: A Managed Rollout With Guardrails and Measurement

Lydatum led a structured adoption program in four parts:

• Tool Selection & Secure Configuration: We evaluated the leading coding agents against the firm's own repositories and selected a standard toolset. It was configured centrally with approved models, permission policies, secrets scanning and tighter restrictions for compliance-scoped services.
• Shared Engineering Context: Repository-level instructions and reusable commands, plus integrations with the firm's ticketing and CI systems, so agents followed the same conventions as the engineers around them.
• Review & Testing Standards: We agreed how AI-assisted changes are reviewed and what test coverage they need, then built those requirements into the delivery pipeline.
• Enablement & Measurement: Engineers trained in cohorts on their own backlog, with a named champion in each team. A shared dashboard tracked adoption alongside lead time and change failure rate.

The rollout started with four pilot teams and expanded as the practices settled. Because measurement was in place from the first day, leadership could see which teams were getting results and which needed more support.

The Impact: Shorter Lead Time at the Same Failure Rate

Four months after the pilot began, 85% of engineers were using coding agents every week.

Lead time from first commit to production fell by 38% with no increase in the change failure rate. Test coverage rose because engineers now ask agents to write the tests that used to be deferred. The firm's next compliance assessment reviewed the new controls without findings, and the same practices have since been extended to the platform and data groups.

Services Delivered: Coding Agent Adoption & Enablement, Agentic SDLC & Engineering Workflows, AI Code Governance & Guardrails, AI Unit Economics & ROI

Technologies Used: Claude Code, GitHub Copilot, GitHub Actions, Model Context Protocol (MCP), SonarQube, Snyk, Datadog