- Updated: March 24, 2026
- 2 min read
NanoClaw Teams Up with OneCLI’s Agent Vault to Secure Docker‑Based AI Agents
NanoClaw Teams Up with OneCLI’s Agent Vault to Secure Docker‑Based AI Agents
In a strategic move to bolster the security and auditability of AI‑driven workloads, NanoClaw has integrated its OneCLI platform with OneCLI’s Agent Vault. The new integration proxies credentials and enforces fine‑grained policies—such as rate limits, approval workflows, and usage caps—for agents that run inside isolated Docker containers.
This collaboration addresses a growing concern among enterprises: how to safely run powerful AI agents without exposing sensitive secrets or losing visibility over their actions. By routing every request through the Agent Vault, NanoClaw ensures that each call is logged, rate‑limited, and subject to pre‑defined approval steps, dramatically reducing the risk of credential leakage or malicious usage.
Key benefits of the NanoClaw‑Agent Vault integration include:
- Credential Proxying: Secrets never touch the agent directly; they are fetched on‑demand from a secure vault.
- Policy Enforcement: Administrators can set per‑agent limits on API calls, data volume, and execution time.
- Audit Trails: Every interaction is recorded, providing a complete, searchable history for compliance teams.
- Container Isolation: Agents run in dedicated Docker containers, isolating workloads and simplifying resource management.
For developers looking to adopt the solution, NanoClaw provides ready‑to‑use SDKs and step‑by‑step guides on the Ubos Tech documentation portal. The integration is also highlighted in our latest security updates blog post, where we dive deeper into configuration best practices.
Read the original announcement on NanoClaw’s blog for a full technical walkthrough: https://nanoclaw.dev/blog/nanoclaw-agent-vault/.
Stay tuned to Ubos.tech for more updates on AI security, container orchestration, and zero‑trust architectures.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.