Lessons tagged “monitoring”
25 published lessons with this tag.
Trigger conditions for schema review
Define specific signals that should prompt you to re-evaluate a schema.
A gate that cries wolf becomes noise
Validation gates are themselves signal sources — and a gate with a high false-alarm rate becomes a noise source that trains you to ignore it. Aviation and intensive care call the result alarm fatigue, and it kills. A self-healing filter network therefore needs a meta-gate: every gate's alarms get logged, its false-positive rate reviewed, and chronic criers retuned or removed. The system must audit not just its contents but its own filters.
Verify delegation is working
Delegation without verification is abdication. Build lightweight checks to ensure delegated work meets your standards.
You cannot improve what you do not monitor
Agent monitoring provides the data you need to optimize your cognitive systems.
Define success metrics for each agent
Every agent needs a clear definition of what success looks like in measurable terms. Without operational metrics, monitoring produces noise instead of signal.
Frequency of monitoring
Monitor too rarely and you miss problems; monitor too often and you create noise. Find the right cadence.
The monitoring dashboard
A dashboard gives you a single view of all your agents' health and performance.
Agent reliability metrics
Track how often each agent fires when it should and does not fire when it should not.
Agent effectiveness metrics
Effectiveness means your agent produces the intended outcome, not just that it runs.
Time-to-fire metrics
Track how quickly each agent responds to its trigger.
False positive rate
An agent that fires when it shouldn't wastes your attention and erodes trust.
False negative rate
An agent that fails to fire when it should leaves you exposed to undetected problems — the silence feels like safety, but it is blindness.
Agent drift
Agents degrade over time unless actively maintained — monitoring catches drift before it becomes failure.
Monitoring overhead
Monitoring itself costs attention and energy — the overhead must be justified by the value it provides.
Automated monitoring
Automate monitoring wherever possible to reduce overhead while maintaining visibility.
Journaling as manual monitoring
Written reflection is the oldest and most versatile form of self-monitoring.
Monitoring creates accountability
The act of measuring creates a commitment loop — what you track, you take responsibility for.
Alert thresholds
Define clear thresholds that distinguish normal operation from problems requiring your attention.
Trend analysis over point-in-time checks
A single measurement tells you where you are; a trend tells you where you are heading.
Monitoring fatigue
Too much monitoring data overwhelms attention and leads to ignoring signals that matter. The solution is not more data — it is fewer, sharper signals routed to the right layer of attention.
Comparative monitoring
Compare agents against each other and against baselines to identify relative performance.
Monitoring informs optimization
Monitoring without action is observation theater — data must drive decisions.
Monitoring is the feedback loop for your agents
Monitoring completes the feedback loop — observation enables adjustment enables improvement.
The first 30 days are critical
New agents are most fragile in their first month — they need extra attention and support to survive.
Post-extinction monitoring
After a behavior is eliminated continue monitoring for signs of return.