5 Signs Your Automation Is Broken But Not Alerting You

Your automation ran. It returned success. No errors in the logs.

But the work never actually happened.

This is the most dangerous failure mode in modern automation — silent, invisible, and expensive to discover. Here are 5 signs your automation is broken right now without alerting you.

1. The Last Run Was Days Ago

Open your n8n, Make, or Zapier dashboard and sort by "last executed." If a workflow that's supposed to run every 15 minutes last ran 3 days ago — that's a stale failure. The trigger stopped firing, or the workflow was silently disabled after an error threshold.

Most platforms won't alert you when a scheduled workflow stops running. They only alert when a running workflow throws an error.

# OpsVeritas detects this automatically:
# "Workflow 'invoice_sync' last ran 72h ago — expected every 15min"

2. HTTP 200 With No Data Changed

Your webhook receiver returned 200. Your Make scenario showed "success." But check the actual output — if the records weren't created in your CRM, the files weren't moved, the Slack message never sent — the workflow completed without doing anything.

This happens when:

• A filter step silently excluded all records
• An API call returned 200 but with an empty data: [] response
• The trigger fired but no items matched the condition

3. Your AI Agent's Output Tokens Are Zero

# This response from OpenAI looks fine:
{
  "status": 200,
  "usage": {
    "prompt_tokens": 847,
    "completion_tokens": 0,  # <-- silent failure
    "total_tokens": 847
  }
}

The agent ran, consumed tokens, charged your account — and produced nothing. This happens more than you'd think: safety filters, context window issues, malformed prompts. If you're not tracking output_tokens per run, you won't catch it.

4. The Same Record Gets Processed Multiple Times

Your "new leads" workflow ran 47 times on the same 3 contacts. No deduplication check, no idempotency key, no seen-record tracking. The CRM now has 47 duplicate entries and 47 notification emails went to your sales team.

This is a success loop — the opposite of a silent failure, but equally invisible until damage is done.

5. Status Shows "Healthy" But Clients Are Complaining

Your monitoring dashboard says all green. Your uptime check passes. But a client just emailed saying their automated report never arrived.

The reason: process-level health checks confirm the server is running. They don't confirm the workflow completed its last expected run. Those are two completely different health signals.

---

How to Catch All Five

The fix is execution-level monitoring — tracking what the workflow actually did rather than just whether the process is alive.

At OpsVeritas, we built exactly this: connect your n8n, Make, Zapier, or GitHub Actions in 2 minutes and we monitor run timestamps, output volume, status transitions (Healthy→Degraded→At Risk), and stale detection.

For AI agents specifically, our SDK tracks output_tokens, cost per run, and agent loops at the execution level — not the server level.

→ Workflow monitoring: app.opsveritas.com
→ AI agent observability: agents.opsveritas.com

Both free to start. 2-minute setup. No infrastructure changes needed.

---

Built this after spending too many hours discovering silent failures from angry clients rather than monitoring dashboards.