{"slug": "onecli-managing-ai-agent-secrets-from-scratch", "title": "OneCLI: Managing AI Agent Secrets from Scratch", "summary": "OneCLI, a Rust-based Docker container, acts as a network gateway to manage AI agent secrets by swapping placeholders for real credentials at the network layer, preventing agents from directly accessing secrets. The tool supports secret sourcing from AES-256-GCM encrypted vaults or real-time sync from 1Password or Bitwarden, and includes a human-in-the-loop approval layer to block sensitive actions until cleared. OneCLI is compatible with tools like Claude Code, Cursor, and OpenClaw, offering a safer alternative to standard env file approaches for production-grade LLM agents.", "body_md": "# OneCLI: Managing AI Agent Secrets from Scratch\n\nInstead of the agent holding the key, OneCLI acts as a network gateway. The agent sends a request with a placeholder, and the gateway swaps it for the real credential at the network layer before forwarding it to the service. This means the agent never actually \"knows\" the secret.\n\n## Deployment and Integration\n\nSince the proxy is written in Rust and packaged as a Docker container, getting it running is straightforward. It supports any tool that can configure an `HTTPS_PROXY`\n\n, making it compatible with a wide range of AI workflows:\n\n**Compatible Tools:**\n\n[Claude](/en/tags/claude/)Code, Cursor, OpenClaw, or any custom LLM agent.\n\n**Secret Sourcing:** You can store secrets directly in the vault (AES-256-GCM encrypted) or sync them in real-time from 1Password or Bitwarden.\n\n**Control Layer:** You can set up human-in-the-loop approvals. Even if an agent decides it needs to perform a sensitive action, the request is blocked at the proxy level until a human clears it.\n\n## Practical AI Workflow Benefits\n\nThe real-world value here is preventing \"permission creep.\" Coding agents are notorious for trying to find creative ways to elevate their own permissions to finish a task. By moving the authorization to a deterministic proxy, you stop relying on the model's \"behavior\" and start relying on hard network policies.\n\nFor example, if you're using a deployment agent, you can allow it to trigger a build but require manual approval before it can actually merge a PR or push to production, regardless of what the agent's internal logic thinks.\n\n## Technical Trade-offs\n\n**Performance:** The Rust core keeps latency low, but it is still an extra hop in the network path.\n\n**Scope:** It doesn't stop an agent from misusing a permission it\n\n*actually*has. If you give the agent broad access to a database via the proxy, it can still delete tables. Tight scoping of policies is mandatory.\n\nFor anyone building a production-grade LLM agent, this is a much safer architecture than the standard \"env file\" approach.\n\n```\nhttps://onecli.sh/\n```\n\n[Next AI SnapSolve: My approach to triple-checking AI answers →](/en/threads/2153/)", "url": "https://wpnews.pro/news/onecli-managing-ai-agent-secrets-from-scratch", "canonical_source": "https://promptcube3.com/en/threads/2166/", "published_at": "2026-07-23 09:00:50+00:00", "updated_at": "2026-07-23 17:10:16.878496+00:00", "lang": "en", "topics": ["ai-agents", "ai-safety", "ai-tools", "ai-infrastructure"], "entities": ["OneCLI", "Claude Code", "Cursor", "OpenClaw", "1Password", "Bitwarden"], "alternates": {"html": "https://wpnews.pro/news/onecli-managing-ai-agent-secrets-from-scratch", "markdown": "https://wpnews.pro/news/onecli-managing-ai-agent-secrets-from-scratch.md", "text": "https://wpnews.pro/news/onecli-managing-ai-agent-secrets-from-scratch.txt", "jsonld": "https://wpnews.pro/news/onecli-managing-ai-agent-secrets-from-scratch.jsonld"}}