Alright, night crew. Let's get into it.
I've been running Lindy agents for a few months now, automating a bunch of our internal support workflows (Jira ticket triage, Slack Q&A, basic runbook lookups). At first, it was magic. Set up the triggers, define the logic, watch it go. But lately? It's been feeling more like a flaky junior dev that needs constant babysitting.
The main issue: silent failures. The agent is supposed to parse a Slack message, decide if it's a known issue, and post a link to the runbook. It works for a week, then just... stops. No errors in the Lindy dashboard, no failed executions logged. It just doesn't trigger. The only way I know is when someone in Slack goes "@here why is the bot dead?" at 3 AM.
My current hypothesis is "context drift." The examples and instructions I gave it months ago don't cover the edge cases that have inevitably popped up. But the system doesn't seem to learn or flag these gaps; it just fails quietly.
My hacky workaround so far is wrapping the agent calls in a small service that does its own logging and alerting:
```python
# Pseudo-code of my watchdog
def call_lindy_agent(prompt):
start_time = datetime.now()
try:
response = lindy_client.execute(agent_id, prompt)
if response is None or response.strip() == "":
# This is where Lindy fails silently for me
raise SilentFailureError("Empty response")
log_success(start_time)
return response
except Exception as e:
log_failure(start_time, e)
page_me() # Because of course
```
Anyone else seeing this pattern? Are you having to build more monitoring *around* Lindy than you expected? Or am I just configuring these things wrong?
NightOps
Oh man, that pseudo-code snippet hits close to home. I've built almost that exact same watchdog wrapper, but for a Jenkins-to-Kubernetes deployment agent.
> silent failures. The agent is supposed to parse a Slack message... It works for a week, then just... stops.
The "works for a week" pattern is exactly what drove me nuts. My theory moved beyond just context drift to include subtle changes in the *input payload* from the triggering service. Slack might change a field format slightly, or Jira Cloud's API response times out a millisecond slower and the agent's parsing logic just gives up without a peep.
Your watchdog approach is the right move. I'd add one thing: make sure it's also monitoring the *absence* of activity. Set up a simple metric that increments every time the agent runs successfully, and alert if that counter hasn't moved in X hours. That catches the "total silence" failure mode faster than waiting for a human to notice.
What are you using for the alerting side of your wrapper? PagerDuty, or something simpler?
—jr
Yeah, monitoring for absence of activity is a great call. I've been burned by that exact thing before.
For alerting, I've had good luck with a simple Grafana alert to Slack. It watches a custom metric from the watchdog script. It's less overhead than PagerDuty for these non-critical workflows, but still gets attention.
Your point about API payload changes is spot on, too. I now log a sample of the raw input payload my agent receives every few runs. Makes it way easier to spot when Slack or Jira changes something on their end.
Logging raw input payloads is the only sane way to operate. But you have to be smart about what you log. If you're just dumping the full payload every run for a high-volume agent, you're going to get murdered on logging costs. I've seen teams rack up five-figure CloudWatch bills that way.
Set a sample rate, log only the unique fields you actually parse, or better yet, log a checksum of the payload structure. If the checksum changes, then you trigger a full capture for investigation. Saves you from the cost death by a thousand log lines.
cost optimization, not cost cutting