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devops-gitops-corpus

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During analysis, observability often reveals issues like:
#### Problem 1: Inefficient Call Patterns
**What observability revealed**: Agent was making redundant API calls, checking customer status multiple times.
**Observability data**:
```json
{
"issue": "redundant_calls",
"pattern": "customer_lookup called 3 times in sequence",
"impact": "300ms additional latency",
"solution": "implement caching layer"
}
```
#### Problem 2: Context Loss
**What observability revealed**: Agent wasn't maintaining conversation context properly.
**Observability trace**:
```json
{
"context_analysis": {
"previous_context": null,
"current_context": "order_inquiry",
"issue": "session_context_not_preserved",
"recommendation": "implement_session_management"
}
}
```
#### Problem 3: Error Handling Gaps
**What observability revealed**: Silent failures when external services were slow.
**Error trace**:
```json
{
"error_analysis": {
"service": "order_status_api",
"timeout": "5000ms",
"actual_response_time": "8000ms",
"result": "silent_failure",
"user_impact": "incomplete_response"
}
}
```
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graph TD
A[Issue Detected] --> B[Observability Analysis]
B --> C[Root Cause Found]
C --> D[Solution Identified]
D --> E[Implementation]
E --> F[Verification through Monitoring]
subgraph "Analysis Tools"
B1[Execution Traces]
B2[Performance Metrics]
B3[Error Patterns]
B4[Decision Logs]
B --> B1
B --> B2
B --> B3
B --> B4
end
classDef problem fill:#fecaca,color:#7f1d1d,font-size:10px
classDef solution fill:#10b981,color:#ffffff,font-size:10px
classDef analysis fill:#34d399,color:#064e3b,font-size:10px
class A problem
class B,B1,B2,B3,B4 analysis
class C,D,E,F solution
`} />
## Testing Strategies for LLM Applications
Based on observability insights, here are effective testing approaches:
### Observability-driven testing
Instead of writing tests first, use observability to understand behavior:
1. **Monitor real interactions** with various inputs
2. **Analyze execution patterns** to understand normal behavior
3. **Identify edge cases** from actual usage data
4. **Document expected patterns** based on successful executions
5. **Create tests** that verify these patterns
This approach helps you write tests that actually matter, not just tests that pass.