Evaluating Performance

Agents are stochastic by nature. You won’t catch issues by looking at a single example — and you can’t rely on instinct or manual checks at scale.

To ship with confidence, you need structured, repeatable ways to evaluate behavior.

Why Evaluation Matters

Evaluation is what separates a working demo from a production system.

• It protects you from regressions
• It helps prioritize prompt or model improvements
• It gives stakeholders visibility into quality and risk

Without it, every change is a gamble.

What to Evaluate

There’s no one-size-fits-all metric, but you should be measuring:

• Correctness: Did the agent reach the right output?
• Helpfulness: Was the response useful or actionable?
• Confidence: Does the agent know when it’s uncertain?
• Consistency: Does the agent behave reliably across similar inputs?

Each agent or use case might need custom scoring logic — especially if the output is free-text.

How to Evaluate Agents

Evaluation spans both traditional and modern methods — and for many enterprise use cases, you’ll want both.

Traditional Metrics (Scikit-learn Style)

If your agent is producing structured outputs (like labels or classifications), you can use standard metrics:

• Accuracy: Overall correctness
• Precision/Recall: Especially useful for imbalanced cases
• F1 Score: Harmonic mean of precision and recall
• Confusion Matrix: Understand types of misclassification

These are great when you have ground truth answers — and many automation use cases do.

Modern Evaluation (RAG, Reasoning, and Free Text)

When your agent generates free text or does multi-step reasoning:

• Use LLM-as-a-judge to score helpfulness, factuality, reasoning clarity
• Use Ragas-style metrics to assess retrieval quality (faithfulness, context relevance)
• Include confidence scoring to monitor model self-awareness

These can be run offline (on gold datasets) or live (on real user interactions).

Replacing Unit Tests with Evaluation Workflows

In traditional software, you’d write unit tests for every function. But agents don’t behave deterministically — so instead, we use evaluation workflows.

A Simple Evaluation Pipeline

Let’s say you’ve built an agent that classifies support requests into categories.

Your evaluation pipeline might look like:

• Create a Golden Dataset
  A JSONL file with 500 real support requests and their expected category labels.
• Run the Agent
  Feed the requests into the agent via an API — log its predictions.
• Compare with Ground Truth
  Use scikit-learn to compute accuracy, precision, recall, and F1 score.
• Report + Alert
  If performance drops >10% from baseline, block rollout.

This can be triggered via CI, scheduled jobs, or manual preview. It replaces brittle unit tests with a robust, outcome-based benchmark.

Example: AutoMarking Eval Strategy

You’re grading free-text answers against a rubric. You want to measure:

Old-school:

• F1 Score (against binary human-assigned outcomes)
• Exact match or rubric-aligned scoring

New-school:

• Reasoning transparency (“Did the agent justify the grade?”)
• Feedback helpfulness (“Would this feedback help a student improve?”)
• Confidence scores vs. human override rate

Let’s say your AutoMarker is upgraded to a new model (e.g. from GPT-3.5 to GPT-4).

Without evaluation:

• You roll it out
• It gives harsher grades on borderline answers
• Your support queue fills up with complaints

With evaluation:

• You run the new version on last semester’s dataset
• You compare scoring distribution and feedback consistency
• You catch the drift — before it impacts real students

This isn’t optional. It’s infrastructure.

With Orcaworks, every agent can be hooked into evaluation pipelines — both pre-launch and post-launch.

That’s how you scale behavior — not just models.