"When a problem depends on a third-party system, the agent often can't complete the loop. It can recommend steps, but it can't reliably apply them, verify them, or keep them correct over time."
"With code, you can run tests, but with a dashboard, what's the equivalent of a unit test? How do you prove it blocks the bad traffic, allows the good traffic, and keeps working when the vendor changes something?"
"At best, you get a checklist. At worst, you're letting an AI drive production config through a UI built for humans, not automation."
AI coding agents primarily operate within code, which is essential for software development. However, many AI tools, especially in security, rely on external dashboards that complicate automation. These tools often require manual intervention, leading to brittle workflows. Verification of changes made through dashboards is challenging, as there is no equivalent to unit tests. This can result in production issues that are difficult to trace. A potential solution is to create agent-friendly APIs, but many integrations still lack proper testing capabilities.
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