AI Agents Looping, Hanging,
or Burning Budget — Silently
Your agent hit an edge case and looped 400 times overnight. You saw the OpenAI bill at the end of the month. By then it was too late.
✓ No credit card · ✓ 5-minute setup · ✓ Works with any LLM framework
AI agents fail in ways that look like success
Loops don't produce errors. Hung tasks look "running." Stalled queues look quiet. Standard monitoring can't see inside an agent — NotiLens can.
Agent looped 400 times, burning API credits
An edge case in your prompt caused your agent to retry the same failed tool call in a loop. The process looked "healthy" — it was running, not erroring. You found the $800 API bill at end of month.
Task queue went silent - agent stopped processing
Your agent processed 3 tasks, then stopped. No error, no crash. It was waiting on a dependency that never resolved. The queue grew to 200 items before a human noticed.
Agent hung on a single task for 6 hours
A single tool call in your pipeline timed out but didn't propagate the error correctly. The agent waited indefinitely. Downstream tasks queued up. Users got no response.
Agent monitoring built for the way LLM pipelines actually fail
NotiLens tracks your agent's lifecycle events, queue throughput, and cost metrics — the exact signals that expose the failure modes standard monitoring misses entirely.
Loop detection
NotiLens detects runaway loops via your signal rules and auto-learned anomaly detection — no manual threshold required.
Task queue silence monitoring
Monitor the rate of task completions. If your agent stops processing tasks — due to a hang, a dependency failure, or a crashed worker — NotiLens detects the silence and alerts your team.
Cost anomaly alerts
Track API call volume per time window. When your token usage or call rate spikes beyond baseline — a sign of runaway loops or unexpected traffic — NotiLens alerts before the bill does.
What NotiLens sends when an agent misbehaves
Every alert fires within 60 seconds of detection — with the specific agent, failure mode, and count so you can intervene immediately.
An AI agent that's looping isn't failing — it's succeeding at the wrong thing, over and over.
Standard monitoring won't catch it. NotiLens watches for loop events via run.loop(), queue silence, and cost anomalies — the signals that show something went wrong inside the agent.
One caught loop pays for the year
A runaway agent looping overnight can generate hundreds of dollars in API costs before anyone notices. Here's what teams typically recover.
loop detected via run.loop() rate — before significant API spend accumulates
run.loop() added to your agent to enable real-time loop detection
to instrument your agent with full lifecycle monitoring
Live in 5 minutes
No framework changes. Instrument key lifecycle points in your agent and set your loop and silence thresholds.
Create a topic
Create an ai-agents topic in NotiLens. Use one topic per agent pipeline for isolated monitoring.
Instrument your agent
Add NotiLens pings at key lifecycle points — task start, tool call, task complete.
nl = notilens.init(name="my-agent",
token="TOKEN", secret="SECRET")
run = nl.task("research-task")
run.start("Starting pipeline")
run.loop("Retrying web search") # loop signal
run.metric("tokens", 1500) # cost tracking
run.complete("Done") # or run.fail() / run.timeout()
Set loop + silence thresholds
Set a signal rule for loop frequency, a silence threshold for task completion, or let anomaly detection handle both automatically.
More ways to monitor your automation stack
AI agent monitoring — common questions
run.loop(msg) each time your agent iterates. NotiLens detects loops via your signal rules and auto-learned anomaly detection. You can set a manual rule (e.g. "alert if more than 10 loop events in 5 minutes"), or let anomaly detection flag when the loop rate deviates from what's normal for that agent — no threshold config needed.Start monitoring your AI agents
Know within 60 seconds when an agent loops, hangs, or burns budget. Free 7-day trial.
Start Free — 7-Day Trial