📊 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:

1. Create a Golden Dataset
   A JSONL file with 500 real support requests and their expected category labels.

2. Run the Agent
   Feed the requests into the agent via an API — log its predictions.

3. Compare with Ground Truth
   Use scikit-learn to compute accuracy, precision, recall, and F1 score.

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

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