Combining retrieval with agent reasoning in Agentic AI - Model Metrics & Evaluation

Metrics & Evaluation - Combining retrieval with agent reasoning

Which metric matters for this concept and WHY

When combining retrieval with agent reasoning, the key metrics are Precision, Recall, and F1 score. These metrics tell us how well the system finds the right information (retrieval) and uses it correctly to answer or act (reasoning). Precision shows how many retrieved items are actually useful, recall shows how many useful items were found, and F1 balances both. This helps us know if the agent is both accurate and thorough.

Confusion matrix or equivalent visualization (ASCII)

Confusion Matrix for Retrieval + Reasoning Output:

               Predicted Relevant   Predicted Irrelevant
Actual Relevant       TP (True Positive)      FN (False Negative)
Actual Irrelevant     FP (False Positive)     TN (True Negative)

Example numbers:
TP = 80, FP = 20, FN = 10, TN = 90
Total samples = 80 + 20 + 10 + 90 = 200

From this:
Precision = TP / (TP + FP) = 80 / (80 + 20) = 0.8
Recall = TP / (TP + FN) = 80 / (80 + 10) = 0.8889
F1 = 2 * (0.8 * 0.8889) / (0.8 + 0.8889) ≈ 0.842

Precision vs Recall tradeoff with concrete examples

Imagine the agent is a helper that finds documents and then reasons to answer questions.

• High Precision, Low Recall: The agent only returns very sure answers. It rarely makes mistakes but might miss some good answers. Good when wrong answers are costly, like medical advice.
• High Recall, Low Precision: The agent tries to find all possible answers, even if some are wrong. Good when missing any answer is bad, like searching for all fraud cases.

Balancing precision and recall depends on the task. F1 score helps find a good middle ground.

What "good" vs "bad" metric values look like for this use case

Good metrics: Precision and recall above 0.8 show the agent finds most relevant info and reasons well. F1 above 0.8 means balanced performance.

Bad metrics: Precision or recall below 0.5 means the agent either misses too much or makes many mistakes. F1 below 0.5 shows poor overall quality.

Example: Precision=0.9, Recall=0.85, F1=0.87 is good. Precision=0.4, Recall=0.7, F1=0.52 is bad.

Metrics pitfalls

• Accuracy paradox: If most data is irrelevant, a model that always says "irrelevant" can have high accuracy but no real skill.
• Data leakage: If retrieval uses future info, metrics look better but model won't work in real life.
• Overfitting: High training metrics but low test metrics mean the agent memorizes instead of reasoning.
• Ignoring reasoning errors: Good retrieval but poor reasoning can still give wrong answers, so measure both parts.

Self-check question

Your combined retrieval and reasoning agent has 98% accuracy but only 12% recall on relevant items. Is it good for production? Why or why not?

Answer: No, it is not good. The very low recall means the agent misses most relevant information, even if it is usually correct when it does find something. This can cause important answers to be lost, which is risky in real applications.

Key Result

Precision, recall, and F1 score best measure combined retrieval and reasoning quality by balancing correctness and completeness.