Monitoring long-running agent tasks
Long-running OpenClaw tasks need monitoring: progress signals, timeouts, and completion handling, so US users can see when a task is running, stuck, or done and take action. Unify task metrics with SingleAnalytics.
Agents sometimes run for minutes or hours: big crawls, batch updates, or multi-step workflows. Without visibility, you don’t know if the task is progressing or stuck. OpenClaw and your skills can emit progress and state so you can monitor long-running tasks and act when something goes wrong. This post covers monitoring long-running agent tasks for US users.
Why monitor long-running tasks
Progress.
“Is it still running?” Heartbeats or progress events (e.g., “processed 100 of 500”) tell you the task is alive and how far along it is. You can decide to wait or cancel.
Timeouts.
A task that runs “forever” might be stuck (e.g., waiting on a dead API). Set a max duration; when exceeded, mark the task failed and notify. Prevents zombie runs and frees resources.
Completion and outcome.
When the task finishes, you want to know: success or failure, summary (e.g., “indexed 1,200 pages”), and where to find output (e.g., Notion page, file). One notification or event so you don’t have to poll.
Debugging.
When a long task fails, logs and events (start, progress, failure) help you see where it broke. US teams use these to fix bugs and tune timeouts and retries.
What to emit
Start.
Emit task_started with task_id, task_type, and timestamp. You can correlate all later events to this run.
Progress (optional).
For tasks with countable steps, emit progress every N steps or every M seconds: e.g., “step 50 of 200” or “processed 1,000 items.” Don’t emit too often (e.g., not every single item) or you flood logs and analytics.
Completion.
Emit task_completed or task_failed with task_id, duration, and outcome (success/failure, summary, error message). If the task produces output (e.g., a report URL), include it.
Timeout.
If the task hits a timeout, emit task_failed with reason “timeout” and the duration. Then notify so you know to investigate.
Where to send events
Logs.
Write to a local or centralized log (e.g., JSON lines). Include task_id, timestamp, and event type. Useful for debugging and audit.
Analytics.
Send task_started, task_completed, task_failed (and optional progress) to your analytics platform. SingleAnalytics lets US teams see run count, duration distribution, and failure rate per task type, so you know which long-running tasks are healthy and which need tuning or fixes.
Notifications.
On completion (success or failure), send a short message to your chat or email: “Task X finished in 12 minutes. Result: …” or “Task X failed after 30 minutes. Error: …” So you don’t have to watch the dashboard.
Implementation notes for US users
- Task identity. Use a stable task_id (e.g., UUID or composite of type + run time) so you can trace one run from start to finish across logs and analytics.
- Progress granularity. For very long tasks (hours), progress every 5–10 minutes or every 10% is enough. For shorter tasks (minutes), progress every N steps may be better. Tune so you get signal without noise.
- Timeouts. Set per task type based on observed or expected duration. Add a buffer; if a task usually takes 10 minutes, timeout at 20 or 30. Alert on timeout so you can increase the limit or fix the cause.
- Measuring impact. Use SingleAnalytics to track duration and success rate over time. Spot regressions (e.g., “this task used to take 5 minutes, now it’s 15”) and tie long-running tasks to business outcomes, so monitoring is actionable.
Summary
Monitoring long-running OpenClaw tasks means emitting start, progress (optional), completion, and timeout events and sending them to logs, analytics, and notifications. US users get visibility into running and stuck tasks and can debug and tune with data. Send task events to SingleAnalytics so long-running task health is visible in one place and reliability keeps improving.