AI agent governance may belong outside the agent code
AI agents become hard to manage when they are deployed like simple backend scripts. A common setup is to build the logic in LangGraph, CrewAI, or raw Python, package it in Docker, and run it on a . That can work for a few agents, but it becomes messy once a team has more than five or ten.
The result can be many stateful agents with high and no standard way to handle secrets, rollbacks, or evaluations. Putting compliance rules, , and deployment steps inside a specific framework such as LangChain creates . The better design is to separate the agent’s main logic from the layer.
Some platforms are moving toward an independent that manages agents from outside the code framework.
Key points
- Managing more than five or ten agents can become difficult with simple script-style deployment.
- Secrets, rollbacks, evaluations, and need a standard operating layer.
- Compliance rules and should not be tightly tied to one .
- Separating agent logic from reduces long-term .
- A can manage many agents from one place.