๐ช๐๐ฌ ๐ ๐ฌ ๐๐ ๐๐๐๐ก๐ง๐ฆ ๐ช๐ฅ๐๐ง๐ ๐๐ข๐๐ ๐๐จ๐ง ๐๐ข ๐ก๐ข๐ง ๐ฆ๐๐๐ฃ ๐๐ง
An agent finished a task at 2am. It wrote the code. It ran checks. Then it stopped. It waited for a process called the Librarian to ship the work in the morning.
This pause is a choice. My agents at aienterprise.dk write files. They read databases. They call APIs. They do not push to production.
Risk is the reason. A wrong file write is fixed in review. A wrong deploy hits your users immediately. These failures are not the same.
I learned this from a mistake. One agent sent a schema update to the wrong site. It used a name prefix instead of a full ID.
I now use a simple system:
- Agent finishes work.
- Agent calls a request script.
- The Librarian process takes over.
- The Librarian checks for conflicts.
- The Librarian ships the code.
The agent never talks to the Librarian. This creates a hard boundary.
The EU AI Act sets rules for high risk systems by 2027. But safety is the main goal. Demos are easy. Production is hard.
Separating the build from the ship creates accountability. Every step has a log. Architecture enforces the rule.
Source: https://dev.to/kimlike/why-my-ai-agents-can-write-code-but-cant-ship-it-598c Optional learning community: https://t.me/GyaanSetuAi