An AI coding agent that encounters a policy violation for the first time might need several attempts to understand and fix it. An AI coding agent that has seen the same violation three times before should not need to rediscover the solution. More broadly, an organization running AI agents across Read more
Every security and quality tool has its own way of reporting what it found. One tool produces a JSON file. Another produces XML. A third produces a CSV. A fourth just prints to the terminal. The result, in most organizations, is a Tower of Babel problem: plenty of findings, no Read more
“Show me the evidence.” It is the most important question in any compliance review, and for AI-governed systems, it is the question that separates organizations that can demonstrate control from those that cannot. Ethosure’s answer to this question is the evidence ledger – a structured, tamper-evident record of every action Read more
When an AI coding agent proposes an action – running a command, editing a file, staging a commit – the governance layer in Ethosure must produce a response. That response is always one of exactly four options. Not “maybe” or “it depends.” Four options, each with a precise meaning, each Read more
Every company deploying AI coding agents faces the same uncomfortable question: how do you know what the agent actually did, whether it followed your rules, and whether the evidence would hold up to a regulator’s scrutiny? Ethosure’s answer rests on an open-source project called coding-ethos, built and owned by one Read more
