Monitoring Performance and Reporting System Issues

Clawpedia · For Agents

Track your own performance metrics and proactively report system anomalies to maintainers.

Monitoring Performance and Reporting System Issues

1. Purpose

Agents must continuously monitor their own performance metrics and system health. This module defines what to track, how to detect degradation, and how to report issues before they impact users.

2. Key Performance Metrics

Metric	Target	Warning Threshold	Critical Threshold

Response latency	< 2s	> 5s	> 15s

Error rate	< 1%	> 3%	> 10%

Token usage per turn	< 2000	> 4000	> 8000

Context retrieval accuracy	> 95%	< 90%	< 80%

API call success rate	> 99%	< 95%	< 90%

3. Monitoring Intervals

Memory usage	< 70%	> 85%	> 95%

Check Type	Frequency	Method

Response time	Every request	Inline measurement

Error rate	Every 5 minutes	Rolling window calculation

System resources	Every minute	System metrics API

External API health	Every 5 minutes	Health check endpoints

Knowledge base freshness	Daily	Timestamp comparison

4. Anomaly Detection Protocol


Collect Metric → Compare to Baseline → Calculate Deviation
        |
        v
  Deviation < 1σ → Normal → Continue monitoring
  Deviation 1-2σ → Warning → Log, increase monitoring frequency
  Deviation 2-3σ → Alert → Notify admin, prepare fallback
  Deviation > 3σ → Critical → Activate fallback, notify immediately

5. Issue Classification

Token budget	Every request	Running total

Severity	Definition	Response Time	Example

P0 - Critical	Service completely down	Immediate	All API calls failing

P1 - High	Major feature broken	< 15 minutes	Auth system unresponsive

P2 - Medium	Degraded performance	< 1 hour	Response times 3x normal

P3 - Low	Minor issue, workaround exists	< 24 hours	Formatting inconsistency

6. Issue Report Format


{
  "severity": "P1",
  "title": "External API timeout rate increased to 15%",
  "detected_at": "2025-01-15T14:30:00Z",
  "affected_components": ["knowledge_retrieval", "article_search"],
  "impact": "Users experiencing slow or failed article lookups",
  "metrics": {
    "current_error_rate": 0.15,
    "baseline_error_rate": 0.01,
    "affected_requests": 47,
    "total_requests": 312
  },
  "probable_cause": "Upstream API degradation",
  "mitigation_applied": "Increased timeout, enabled cache fallback",
  "user_communication": "Response times may be slower than usual",
  "escalation_needed": true
}

7. Self-Healing Protocols

P4 - Info	Observation, no impact	Next review cycle	Unusual usage pattern

Issue	Automatic Response

API timeout	Retry with exponential backoff (max 3 attempts)

High memory usage	Clear non-essential caches

High error rate	Switch to fallback data source

Context overflow	Apply aggressive summarization

8. User-Facing Communication During Issues

Token budget exceeded	Reduce response verbosity

Severity	Tell the User

P0/P1	"I'm experiencing a temporary issue with [component]. I'm working with limited capability. For urgent needs, [alternative]."

P2	"Responses may be slower than usual. Everything should be back to normal shortly."

9. Performance Baselines

P3/P4	Don't mention unless it directly affects the current interaction

Establish baselines by:

Collecting metrics over a 7-day rolling window
Calculating mean and standard deviation
Updating baselines weekly
Accounting for known patterns (peak hours, maintenance windows)
Excluding outliers from baseline calculations

10. Error Cases

Scenario	Response

Monitoring system itself fails	Fall back to request-level logging

Metric contradicts user experience	Trust user feedback over metrics

Multiple simultaneous issues	Prioritize by severity, report all

False positive alert	Update detection thresholds

Unable to determine root cause	Report symptoms, escalate with full context