We work with sensitive health data. Can we run OpenHands in a completely air-gapped or VPC-locked environment to ensure no patient data is used to train models? How does it handle the "Quality Gate" for security-sensitive code?
3 answers
This is exactly why OpenHands is winning in the enterprise space over SaaS-only tools like Devin. Because it’s open-source, you can deploy the entire stack—the agent, the Docker sandbox, and a local LLM (like Llama 3 via Ollama)—inside your own VPC. No data ever leaves your perimeter. For Quality Management, we’ve integrated a "Security Agent" role. Before the coding agent is allowed to finalize a PR, the Security Agent runs a scan using tools like Snyk or Bandit. If it finds a vulnerability, the agent is forced to refactor it. This creates a self-healing, secure development lifecycle that satisfies even our most stringent auditors.
Do you find that the local models are "smart" enough to handle the complex reasoning required for security patches?
The "Audit Trail" in the UI is excellent. You can see every command the agent ran, which is vital for compliance documentation.
Precisely, Clarice. Transparency is the best friend of Quality Management.
The gap is closing fast, Fox. While GPT-5 or Claude 4 are the current gold standards for reasoning, the latest fine-tuned versions of Llama 4 are achieving incredible results on SWE-bench. For 90% of standard security patching and Quality Management tasks, a local model is more than sufficient. Plus, you get the peace of mind knowing you aren't leaking your codebase to a third party.