I'm evaluating both frameworks for potential enterprise use, and the default network posture is a primary concern. From my review, neither starts from a zero-trust stance, but their approaches differ significantly.

In AutoGen, the core framework doesn't inherently manage network calls. The security of inter-agent communication is largely dependent on the underlying LLM provider's API security. However, the `UserProxyAgent` with code execution introduces a critical network vector. By default, code executed via `code_execution_config` can make arbitrary outbound calls unless explicitly restricted by a sandbox (like Docker) that you must configure yourself. The default is permissive.

CrewAI handles this differently through its `crew` abstraction. Network access is indirectly governed by the tools you grant to agents. The default tools don't include arbitrary web requests, but an agent with a `RequestsTool` or similar has no inherent network policy limiting targets. The security boundary is the tool permission system, which is coarse-grained.

Key default policy gaps I've noted:
* AutoGen's code-executing agents allow unrestricted outbound calls from executed code unless sandboxed.
* CrewAI agents with web-enabled tools can call any reachable endpoint.
* Neither framework has default allow/deny lists for IPs or domains at the framework level.
* Inter-agent messages in both frameworks are passed in plain text within the runtime, posing an internal data leakage risk if the host is compromised.

The question is, which requires less rework to lock down for a compliant deployment? AutoGen seems to need immediate sandboxing, while CrewAI requires strict tool governance. DS