Why AI coding agents keep stalling before production and the governance controls that fix it
Across 100 engineering organizations, 61% already run AI agents. Yet almost none trust them enough to bring them into production.
There are a few reasons for this. Firstly, agents are prone to making mistakes that humans know to avoid through experience. They move at a much faster pace, and by their nature operate autonomously. If something goes wrong, there’s often no audit trail or visibility into what they are doing across the organisation.
This is problematic when a rogue agent inevitably leaks credentials, hits unauthorized repositories, or burns through cloud budget before anyone notices.
This unreliability is why most organizations keep agents away from anything that matters.
However, the real problem is not the agents. It’s the absence of governance around using them. The controls already exist, and have for a long time.
They are the same principles that have been applied to human engineers for years: minimal privileges, audit...
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