Alternatives to Building Your Own Agent Approval System

Before you write a pending_actions table and a Slack bot, see the four real alternatives to a custom AI agent approval system and when each one actually beats rolling your own.

Why teams end up building one

The trigger is almost always the same: an agent got far enough to be useful, then did something (sent an email, posted a comment, wrote a row) that nobody had reviewed first. The fix people reach for first is a bespoke approval system — a database table, a status column, a Slack webhook, a cron job that pings the agent for the answer. It works for a weekend demo. Then someone needs it on their phone, someone else needs an audit log, and the "just a table and a webhook" system has grown a UI, a notification layer, and an expiry policy nobody remembers writing.

That's the point to stop and look at what already exists, instead of continuing to build.

Option 1: a workflow engine's human-in-the-loop node

Tools like n8n, Temporal, and Inngest have first-class "wait for human input" primitives — n8n's Wait node with a webhook resume, Temporal's signal-based approval workflows. If your agent already lives inside one of these, this is often the path of least resistance: no new service to run, the approval step is just another node.

The tradeoff: these are workflow engines first. You get a pause-and-resume primitive, not a purpose-built inbox — no mobile-friendly approve/reject cards, no diff view for an edited draft, no dedicated audit trail of decisions (as opposed to workflow runs). Adopting one just for the approval step is a heavier dependency than the problem calls for.

Option 2: roll your own (the DIY path)

The minimal version is genuinely simple to prototype:

# db: actions(id, kind, title, body, status, created_at)
import sqlite3, time, uuid

def push_action(kind: str, title: str, body: str) -> str:
    action_id = str(uuid.uuid4())
    db.execute(
        "INSERT INTO actions (id, kind, title, body, status) VALUES (?, ?, ?, ?, 'pending')",
        (action_id, kind, title, body),
    )
    notify_slack(f"New action pending: {title} — {action_id}")
    return action_id

def await_decision(action_id: str, timeout_s: int = 3600) -> str:
    deadline = time.time() + timeout_s
    while time.time() < deadline:
        row = db.execute("SELECT status FROM actions WHERE id = ?", (action_id,)).fetchone()
        if row[0] != "pending":
            return row[0]
        time.sleep(10)
    return "expired"

This is real code you could ship today. The cost shows up later: a reviewer's phone needs a UI, not a database row, so you build one. Someone approves the same batch of emails every morning, so you build bulk-decide. Legal asks who approved what and when, so you build an audit log. Each of those is a normal, boring feature — and each one is a week you didn't spend on the agent itself. Roll-your-own is the right call when the surface is genuinely tiny (one action kind, one reviewer, no compliance requirement) and staying that way.

Option 3: repurpose existing approval/ticketing software

Some teams point agent actions at a system already in place for other approvals — a ticketing tool, a lightweight BPM product, even a shared spreadsheet with a manual review column. This avoids new infrastructure, but these tools weren't built for the agent loop: no GET status endpoint an agent can poll cleanly, no concept of "here is the edited version, execute this text," no expiry that maps to "this draft is stale, don't send it." You end up gluing scripts around a tool that wasn't designed for this shape of problem, which is its own maintenance burden.

Option 4: a dedicated approval-gate service

The fourth option is a service built specifically for this loop: agent proposes, human decides, agent executes only on approval — nothing else. Impri is this option. It exists so you don't build the table, the Slack bot, the diff view, and the audit log yourself:

curl -X POST https://api.impri.dev/v1/actions \
  -H "Authorization: Bearer $IMPRI_API_KEY" \
  -d '{
    "kind": "email.send",
    "title": "Reply to support ticket #4821",
    "preview": { "format": "markdown", "body": "Hi Jordan, thanks for..." },
    "editable": ["preview.body"],
    "expires_in": 3600
  }'

Three calls — push, poll, report — and you get a mobile-friendly inbox, human edits with a diff, notification channels (Slack, Telegram, Discord, email, push), and an audit trail, without owning that surface yourself. It's honestly narrow, though: Impri is only the gate. It does not generate the draft or orchestrate multi-step workflows — pair it with n8n or your own agent code for that, the same way you would with a DIY table.

Which one to pick

If you're weighing this decision in more depth — including the actual engineering-hours math — see build vs. buy for an AI agent approval queue. For the mechanics of wiring a gate into your agent once you've picked one, the MCP integration is the fastest path if your agent already runs inside an MCP client.