Choosing the right tool for AI agent control

Different jobs need different kinds of control. The sections below are four common situations. Each one names a type of tool, what it is good at, and what it does not try to solve. Read them in order, or jump to the one that matches your situation.

I want to lock down AI coding agents on my own Mac.

Agent Safehouse

A macOS-only sandbox that wraps local AI coding agents in a kernel-enforced deny-first policy. Single shell script, zero dependencies, uses Apple's built-in sandbox-exec.

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What it does well

Kernel-level enforcement on macOS via sandbox-exec, not a wrapper or proxy
Zero dependencies, single self-contained shell script
Tested against major coding agents including Claude Code, Codex, Cursor, Gemini CLI, Cline, and Aider
Open source under Apache 2.0

What it does not cover

macOS only - does not work on Linux or Windows
Designed for individual developer machines, not teams or shared environments
No consent UX, organisation-wide policies, or audit trail you can share with a security team
No spending controls for agent API usage
The author describes it as a hardening layer, not a security boundary against a determined attacker

I want kernel-level enforcement for agents running in CI and pipelines.

agentsh

Execution-layer security for AI agents in CI, containers, and pipelines. Drops into sandboxes you already run (Vercel, E2B, Daytona, Cloudflare, Modal, and others) and enforces policy at the syscall level using Landlock, seccomp, and similar Linux primitives.

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What it does well

Drop-in SDK that wraps existing sandbox providers - does not replace your sandbox
Enforces at the kernel level via Landlock, network proxy, and shell shim, with seccomp and ptrace where available
Built for headless agent runs in CI and pipelines where there is no human to prompt
Open source, with a parallel commercial offering (Beacon, Watchtower) for fleet-wide control

What it does not cover

Linux-focused - designed for server, container, and CI environments, not local macOS coding workflows
No consent UX for non-technical end users
No spending controls for agent API usage
Requires infrastructure knowledge to deploy and write policies

I want developer-focused approval workflows with Slack and Discord routing.

AgentGate

Open-source human-in-the-loop approval system. Agents request, policies decide, humans approve via Slack, Discord, email, or dashboard.

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What it does well

Multi-channel approval routing (Slack, Discord, email, web)
Policy engine for auto-approve and auto-deny rules
TypeScript SDK and MCP server included
Self-hosted with Docker, full audit trail

What it does not cover

Developer-focused - no consent UX designed for non-technical end users
No spending controls or budget enforcement
Self-hosted only - no managed option for teams without ops capacity

I want org-level governance with consent UX and audit trails.

Multicorn Shield

A control layer for AI agents in teams. Shield adds consent screens, spending limits, permissions, and activity logging so people can approve risky work and you can review what happened later.

What it does well

Consent screens before an agent acts, written for people who are not security engineers
Organisation-wide policies and roles
Tamper-evident activity logging for audit and review
Spending limits and budgets for agent usage
MCP-aware path so tool calls can be governed in one place
Works across macOS, Linux, and Windows so distributed teams use the same controls
Designed for teams from day one, not retrofitted from a single-developer tool

What it does not cover

Not a personal sandbox for locking down your laptop like a dedicated local isolation product
Not syscall-level container enforcement; it governs agent behaviour and tool access, not the kernel
You need to connect it to how your agents and MCP servers run in your environment
Not as lightweight as a self-hosted developer tool - Shield is built for teams that need managed infrastructure and compliance support

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