When an organization deploys software to govern its AI agents, a natural question follows: how do you know the governance software itself is trustworthy? How do you know the version you installed is the version the developers built and signed? How do you know it does not contain a vulnerable Read more
Ask a financial institution’s chief information security officer whether they are comfortable routing AI agent governance decisions through a cloud service operated by a third party, and the answer will usually be no – or at minimum, a long conversation about data residency, vendor risk, and regulatory compliance. Ask the Read more
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
Governance problems in software organizations often share a root cause: rules defined in one place are enforced differently in another, not enforced at all in a third, and unknown to a fourth. A security policy lives in a Confluence page; the Git hook that supposedly enforces it was written two Read more
Most organizations have a set of engineering principles – stated values that describe how software should be built. Things like “fail early and loudly,” “no undocumented dependencies,” “security is everyone’s responsibility.” These principles typically live in documentation, onboarding guides, and the institutional knowledge of experienced engineers. What they rarely do Read more
Git hooks and agent hooks catch problems at commit time and during active tool calls. But a complete governance picture for AI agents also needs visibility into the traffic flowing between the agent and the AI provider – the prompts being sent, the responses coming back, the tool calls being Read more
Version control is the foundation of every software project. It is where code goes from “written” to “official.” For organizations deploying AI coding agents, it is also one of the most critical enforcement points – because a commit that lands in the main branch is, in many senses, a fact. Read more
A well-governed AI agent should not need to discover its limits by hitting them. If an agent is about to propose a command that will be blocked by policy, it is better for the agent – and for the organization – if the agent can ask first, adjust its plan, Read more
You have evaluated an AI agent’s proposed action against your policies, and the answer came back: ALLOW. The action is permitted. Now what? In a naïve governance system, “allowed” means the action runs without further constraint. But in any real-world deployment, this creates a problem: the system’s behavior after approval 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
