Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
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nWave-ai/nWave622Updated Sep 16, 2026
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nw-investigation-techniques
Evidence collection methods, problem categorization, analysis techniques, and solution design patterns
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Investigation Techniques
Problem Categorization
Technical Problems
| Category | Sub-Category | Common Symptoms |
|---|---|---|
| System Failures | App crashes, memory leaks, deadlocks, data corruption | Service unavailability, resource exhaustion, integrity errors |
| System Failures | Hardware, network, database, security | Connectivity loss, capacity limits, access failures |
| Performance | Response time: slow queries, latency, algorithmic inefficiency | High p95/p99, user-reported slowness |
| Performance | Throughput: thread pool exhaustion, connection limits, queue backlog | Reduced capacity, growing queues |
| Integration | Internal: component comms, data format, version conflicts | Interface errors, serialization failures |
| Integration | External: third-party availability, API changes, auth failures | Timeouts, contract violations |
Operational Problems
| Category | Common Symptoms |
|---|---|
| Deployment: script failures, config drift, migration errors | Failed releases, environment inconsistencies |
| Monitoring: alerting gaps, backup failures, incident response | Missed incidents, slow recovery |
| Human factors: communication gaps, knowledge silos, skill gaps | Repeated mistakes, slow onboarding |
Evidence Collection
Technical Evidence Sources
Logs: application (timestamp correlation) | system/infrastructure | database | network traces
Metrics: performance/resource utilization | error rates/response time trends | user behavior/transaction patterns | infrastructure health/capacity
Configuration: system/deployment settings | code changes/VCS history (git log, blame) | env vars/dependencies | security/access controls
Evidence Validation
- Cross-reference: verify from multiple independent sources
- Timestamp validation: confirm event sequence accuracy
- Completeness check: identify data gaps/corruption
- Correlation vs causation: distinguish co-occurrence from causation
Analysis Techniques
Quantitative
- Trend: time series of metrics, error pattern frequency
- Distribution: response time percentiles, error rate across components
- Pattern recognition: log anomalies, behavior patterns, error clustering
Qualitative
- Timeline reconstruction: detailed incident timeline, correlate changes with symptoms
- Process analysis: workflow disruptions, communication flow, decision chains
- Environmental: recent changes, system load, external factors, related incidents
Solution Design Patterns
Immediate Mitigations (restore service)
Quick fixes | workarounds to minimize impact | emergency procedures | monitoring enhancements
Permanent Fixes (prevent recurrence)
Architecture modifications | code quality/defensive programming | config management/environment consistency | testing/validation improvements
Early Detection (catch faster)
Leading indicators | anomaly detection/predictive alerting | automated quality gates | threshold tuning from learnings
Solution Prioritization Matrix
| Priority | Criteria | Action |
|---|---|---|
| P0 | Active incident, users impacted | Immediate mitigation, hours |
| P1 | Root cause fix for recurring issue | Permanent fix, current sprint |
| P2 | Prevention for potential issues | Next sprint |
| P3 | Systemic improvement | Backlog with evidence |
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