Running OpenClaw agents autonomously is powerful—but autonomous doesn't mean unattended. The engineers who get the most out of AI agents aren't the ones who set and forget. They're the ones who build a lightweight observation habit that catches small problems before they become big ones.
This guide describes a practical daily routine that takes about 2 minutes with ClawBridge.
Why Daily Checks Matter
When you go days without looking at your agents, small issues compound:
- A memory journal entry that would have taken 30 seconds to correct gets reinforced over dozens of sessions.
- A slow token cost creep—say, 20% more than expected—goes unnoticed for weeks and adds up to a meaningful overage.
- A session that ended on an error and wasn't restarted means a whole day of expected tasks didn't run.
None of these are catastrophic on their own. But they're all easily caught with a quick morning glance at ClawBridge.
The Routine
Open ClawBridge on your phone each morning (or whenever you start your day). Run through these three stops:
Stop 1: Live Thoughts — Is the Agent Active and Healthy? (~30 seconds)
Open the Live Thoughts feed. If your agent is running a scheduled task, you should see reasoning activity. Look for:
- Normal: Distinct steps, clear reasoning, tool calls that make sense in context.
- Warning sign: Repetitive reasoning, the same tool call appearing multiple times in sequence (possible loop).
- Problem: Completely empty feed when you expect activity (agent may have crashed or stalled).
Stop 2: Memory Feed — Did Anything Unexpected Get Written? (~60 seconds)
Check the Memory Feed for yesterday's journal entries. Skim for:
- Any new "rules" the agent wrote for itself that seem off or overly broad.
- Any memory updates that contradict instructions you care about.
- Anything that looks like the agent drew the wrong conclusion from a task.
This takes less than a minute once you know what a "normal" day's journal entry looks like for your agent.
Stop 3: Token Economy — Is Spending in the Expected Range? (~30 seconds)
Glance at yesterday's token cost in the Token Economy panel. Ask one question: is this within the normal range for a day with this type of task load? If not, dig in—something ran longer or hit an unexpected loop.
Making It a Habit
The key is consistency, not depth. You're not doing a full audit every day—you're doing a quick anomaly scan. Anomalies stand out quickly once you have a baseline sense of what "normal" looks like for your setup.
Over time you'll develop pattern recognition: this is what my memory journal looks like after a coding task, this is my normal daily token range, this is what healthy Live Thoughts output looks like for a research agent. That baseline is your early warning system.
Bonus: What to Do When You Spot Something
- Suspicious loop in Live Thoughts? → Mission Control → Emergency Stop → restart with a corrected prompt.
- Bad memory entry? → Edit the memory file directly on the server, or close the session and start fresh.
- Unexpected token spike? → Cross-reference with Live Thoughts history—identify which task caused it and whether it completed correctly.
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Two minutes a day keeps the incident reports away.