Overview
Evaluation-Driven Development and Operations (EDDOps) is a process model that integrates evaluation throughout the LLM agent lifecycle. Rather than treating evaluation as a final checkpoint, EDDOps makes it central to design, development, and operations.
The EDDOps Lifecycle
┌─────────────────────────────────────────────┐
│ DESIGN PHASE │
│ Requirements → Evaluation Criteria │
└─────────────────────┬───────────────────────┘
▼
┌─────────────────────────────────────────────┐
│ DEVELOPMENT PHASE │
│ Build → Evaluate → Iterate │
└─────────────────────┬───────────────────────┘
▼
┌─────────────────────────────────────────────┐
│ DEPLOYMENT PHASE │
│ Deploy → Monitor → Evaluate → Improve │
└─────────────────────┬───────────────────────┘
│
└──────► Feedback Loop
Six Evaluation Drivers
D1: Lifecycle Coverage
Span pre-deployment, post-deployment, and continuous operation. Don't just evaluate before launch—monitor throughout.
D2: Metric Mix Beyond Aggregates
Combine:
- End-to-end outcomes (task success rate)
- Intermediate step-level checks (tool call accuracy)
- Slice-aware analysis (performance by user segment)
D3: System-Level Anchor
Anchor evaluation in system behavior, not isolated model performance. Use model-level probes to explain system-level outcomes.
D4: Temporal Awareness
Track how performance changes over time:
- Concept drift detection
- Seasonal patterns
- Degradation alerts
D5: Multi-Stakeholder Perspectives
Different stakeholders care about different metrics:
- Users: Task completion, response time
- Ops: Error rates, resource usage
- Business: Cost, conversion rates
- Safety: Harm incidents, policy violations
D6: Continuous Improvement Loop
Evaluation findings should automatically trigger:
- Prompt refinements
- Agent architecture changes
- Training data updates
- Threshold adjustments
Evaluation Types
Offline Evaluation
- Benchmark suites: Standardized test sets
- Regression testing: Compare new vs. old versions
- Adversarial testing: Red-team probing
- Human evaluation: Expert review of samples
Online Evaluation
- A/B testing: Compare variants in production
- Shadow mode: Run new agents in parallel without serving
- Canary deployment: Gradual rollout with monitoring
- Real-time metrics: Live dashboards and alerts
Agent-Specific Evaluations
Skill Evaluation
Test individual agent capabilities in isolation.
Trace Analysis
Review complete execution traces for:
- Planning quality
- Tool use correctness
- Error handling
- Context management
Router Evaluation
For systems with branching logic:
- Routing accuracy
- Fallback behavior
- Edge case handling
Tooling Ecosystem
| Tool | Strength |
|---|---|
| OpenAI Evals | Custom eval frameworks |
| DeepEval | Unit testing for LLMs |
| InspectAI | Multi-turn evaluation |
| Phoenix | Trace analysis |
| GALILEO | Production monitoring |
Implementation Checklist
- Define evaluation criteria during requirements
- Build benchmark suites before implementation
- Instrument agents for trace collection
- Set up continuous evaluation pipelines
- Create feedback loops to development
- Monitor production metrics with alerting