💻 Offline vs Online Evals
When working with LLM-based systems and agents, it's not enough to evaluate performance once — you need ongoing, structured feedback. This is where offline and online evals come in.
Each serves a different purpose. Together, they form a complete picture of how your system behaves — both in development and in the wild.
📦 What Are Offline Evals?
Offline evals are static, curated datasets designed to be run repeatedly during development.
- ✅ Predictable: You run them on known inputs and expected behaviors.
- 🔁 Repeatable: You can rerun the same test set after any code, prompt, or model change.
- 🧪 Controlled: You isolate variables to see exactly what changed and why.
Common Use Cases
- Testing changes to prompts or logic
- Benchmarking a new model
- Regression testing before deploys
- Developing new evaluators or metrics
Think of offline evals as your unit tests and benchmarks for LLM agents.
🌐 What Are Online Evals?
Online evals are dynamic evaluations that operate over live system data — your real-time or recent plans and executions.
- 🚀 Production-aware: They track real user traffic and system behavior.
- 🧭 Continuous: As new plans and runs are created, they’re automatically evaluated.
- 🧩 Unfiltered: They expose blind spots not covered by test datasets.
Common Use Cases
- Monitoring quality in production
- Detecting silent failures or regressions
- Measuring alignment with user goals
- Spotting drift over time (e.g., LLM or data changes)
Think of online evals as your observability layer for agents in the real world.
🔍 Key Differences
| Feature | Offline Evals | Online Evals |
|---|---|---|
| Input Source | Static, curated test cases | Live or recent production data |
| Frequency | Manually or on CI | Continuous or scheduled |
| Use Case | Development, testing, iteration | Monitoring, regression, drift detection |
| Control | High — inputs & expectations known | Low — inputs and outputs are emergent |
| Scope | Targeted tasks and edge cases | Real-world coverage |
🧠 Why We Need Both
Relying on just one is risky:
- Only offline means you’re blind to real-world edge cases, user behavior, and model drift.
- Only online means you lose the precision and control needed to improve the system safely.
Used together, they give you:
- Confidence in what you changed
- Visibility into what’s happening
- Tools to iterate intelligently and respond quickly
🤔 When Should You Use Each?
| Scenario | Use This Eval Type |
|---|---|
| Refactoring prompts | 🧪 Offline |
| Validating new tool logic | 🧪 Offline |
| Benchmarking multiple models | 🧪 Offline |
| Tracking production quality over time | 🌐 Online |
| Detecting drift from user feedback | 🌐 Online |
| Diagnosing regressions after a release | 🌐 + 🧪 Both |
| Building dashboards or leaderboards | 🌐 Online |
🧬 Evals as a Lifecycle
- Offline evals keep your development grounded.
- Online evals keep your system honest in production.
Together, they support a feedback loop that’s essential for intelligent, adaptable agents.
Treat evals as part of your build-measure-learn cycle — not an afterthought.