I run AI agents connected to databases, APIs, and filesystems. The problem: every agent gets full access to everything, with no enforcement layer between intent and execution.
SentinelGate is a firewall that sits between the agent and the system. Start the server, wrap any agent:
sentinel-gate start # MCP proxy only
sentinel-gate run -- python my_agent.py # full protection
It auto-detects the runtime (Claude Code, Gemini CLI, Python, Node.js) and injects interceptors. Every action is evaluated against CEL policy rules before execution. Denied actions never happen.
Three layers: MCP proxy, HTTP gateway, runtime hooks — one port, one Go binary, one Admin UI with audit trail.
Honest about limitations: runtime hooks are best-effort — a hostile agent can bypass them via FFI or raw sockets. The HTTP gateway adds network-level enforcement. Together they give you defense in depth, not an OS-level sandbox.
Hi HN, I'm Andrea.
I run AI agents connected to databases, APIs, and filesystems. The problem: every agent gets full access to everything, with no enforcement layer between intent and execution.
SentinelGate is a firewall that sits between the agent and the system. Start the server, wrap any agent:
It auto-detects the runtime (Claude Code, Gemini CLI, Python, Node.js) and injects interceptors. Every action is evaluated against CEL policy rules before execution. Denied actions never happen.Three layers: MCP proxy, HTTP gateway, runtime hooks — one port, one Go binary, one Admin UI with audit trail.
Honest about limitations: runtime hooks are best-effort — a hostile agent can bypass them via FFI or raw sockets. The HTTP gateway adds network-level enforcement. Together they give you defense in depth, not an OS-level sandbox.
AGPL-3.0. Commercial licenses available.
What's missing?