n8n workflows run on a schedule, in the background, often after a handover. AgentPing tells you when one stops firing, stalls or errors, and what each AI run cost, from a single HTTP Request node. No SDK, no self-hosting.
An n8n workflow can stop firing after an edit, stall on a slow step, or loop quietly while the rest of your automation looks fine. The executions list only tells you about runs that happened, so the failure that matters most, the run that never started, leaves no trace to find.
Add one HTTP Request node (or a final webhook step) that pings AgentPing. A bare ping gives you missed-run and failure alerts; pass the model and the token counts from your AI node and AgentPing prices the run for you, no cost maths in the workflow.
no SDK, just an HTTP Request node
# Heartbeat on every run (HTTP Request node, or curl from any step): curl "https://eu.ingest.agentping.io/v1/ping?key=ping_…&agent=lead-enrichment&status=ok" # Send the model + token counts from your AI node; we price it from your rate card: curl "https://eu.ingest.agentping.io/v1/ping?key=ping_…&agent=lead-enrichment&status=ok\ &provider=openai&model=gpt-4o&input_tokens=1240&output_tokens=380" # Already have a dollar figure? Pass cost_usd instead and it wins: curl "https://eu.ingest.agentping.io/v1/ping?key=ping_…&agent=lead-enrichment&status=ok&cost_usd=0.0123"
Full setup, environment variables and options are in the n8n setup docs.
Every one of these leaves your executions list looking either healthy or empty, which is why they get found by a downstream complaint rather than by monitoring.
A workflow deactivated during an edit, a schedule trigger changed and never switched back on, or an instance restarted without the workflow resuming. There is no failed execution to alert on, because there is no execution at all. Absence is the signal, and it is the one signal n8n cannot raise about itself.
An OAuth token lapses or an API key is rotated. The workflow still fires on schedule and still errors on the same node every time. If nobody opens the executions list, it keeps failing on schedule indefinitely.
An IF or Switch node starts sending every item down a path that does no work. The execution is green, the run count is normal, and the output volume has silently gone to zero.
The model responds, the JSON parses, the workflow succeeds, and the content is wrong. Nothing in the execution log distinguishes a good answer from a plausible bad one. This is the failure mode that scales with how much you rely on the agent.
You do not need all of this on day one. Start with the heartbeat, which is one node and catches the failure that hurts most.
| Signal | How you send it | What it catches |
|---|---|---|
| Run happened | HTTP Request node at the end of the workflow | Trigger stopped firing, workflow deactivated, instance down |
| Run status | status=ok or status=failed on the ping | Credential expiry, failing node, repeated errors |
| Expected schedule | Set the cadence on the agent in AgentPing | A run that never started, within minutes of the window closing |
| AI cost | provider, model and token counts on the ping | Model spend per workflow, per client, per feature |
| Output quality | Send the output and give the run a goal | The successful run that produced a bad answer |
n8n records what ran. Monitoring is about what should have run and did not, and about being told without looking.
The executions list is somewhere you go. Monitoring comes to you. If finding out requires remembering to check, you will find out late, and on the days you are busiest you will find out latest.
A workflow that never ran creates no row. An empty list looks identical to a quiet week. AgentPing holds the expected cadence separately from the runs, so a gap is a fact it can alert on.
An execution shows the nodes that ran, not what the model call cost. Send the model and token counts on the ping and AgentPing prices the run server side from a maintained rate card, so spend rolls up per workflow without cost maths inside n8n.
Send one run from n8n and see cost, status and latency. Then add an alert for cost spikes or missed runs.
Monitoring n8n across many clients → Pulse, live monitoring → Spend, cost attribution → Verify, quality scoring →