After evaluating both frameworks for a secure multi-agent deployment, my audit findings led me to select AutoGen. While CrewAI offers an intuitive, role-based workflow, its security model proved too permissive by default for a zero-trust context. AutoGen, while not without significant flaws, provided a more granular foundation for implementing secure communication and execution boundaries.

The primary deciding factors were in the areas of message provenance and code execution control:

* **Inter-Agent Messaging Trust**: CrewAI's `Agent`-`Task`-`Crew` model inherently trusts all intra-crew communication. There is no built-in mechanism for signing or validating messages between agents. In AutoGen, while also unsigned by default, the `ConversableAgent` architecture allows for explicit validation hooks on every message exchange. I could implement a pattern to verify a JWT attached to each message, ensuring non-repudiation and integrity.
```python
# AutoGen: Example pre-processor for JWT validation
def validate_message_sender(message, sender, receiver):
if "token" not in message:
raise ValueError("Unsigned message rejected")
# Verify JWT signature, issuer (sender), audience (receiver)
payload = jwt.verify(message["token"], key=KEYS[sender], audience=receiver.name)
message["verified_sender"] = payload["sub"]
return message
agent.add_preprocessor(validate_message_sender)
```

* **Code Execution Sandboxing**: AutoGen's `UserProxyAgent` with `code_execution_config` can be directed to use Docker or a restricted subprocess, a critical containment layer. CrewAI's tool execution, by contrast, runs in the same process as the orchestrator. In AutoGen, disabling local execution or enforcing a time-bound Docker container is a clear, configurable security boundary.
```python
# AutoGen: Configuring contained code execution
code_execution_config={
"work_dir": "sandbox",
"use_docker": "python:3-slim",
"timeout": 30,
"last_n_messages": 1
}
```

CrewAI's role and permission design is conceptually useful but operates at an abstraction level too high for technical security controls. Permissions are about access to tools or other agents, not about validating the content or origin of requests. Its audit logging, while present, does not capture the cryptographic provenance needed for a forensic timeline.

Ultimately, both frameworks exhibit default-unsafe patterns: CrewAI in its implicit trust model, and AutoGen in its initially open code execution. However, AutoGen's extensible agent lifecycle hooks and explicit execution environment configuration provided the necessary levers to implement a zero-trust architecture, where every message and code action must be explicitly validated and contained. For a less sensitive, rapid-prototyping scenario, CrewAI's productivity benefits might outweigh these concerns, but they were disqualifying for our use case.