Research assistant agent in Agentic Ai - Model Metrics & Evaluation

Metrics & Evaluation - Research assistant agent

Which metric matters for Research assistant agent and WHY

A research assistant agent helps find and summarize information accurately and quickly. The key metrics to check are Precision and Recall. Precision tells us how many of the agent's answers are actually correct and relevant. Recall tells us how many of the important facts or documents the agent found out of all that exist. We want both to be high so the agent gives useful and complete information without many mistakes.

Confusion matrix for Research assistant agent

      | Predicted Relevant | Predicted Not Relevant |
      |--------------------|------------------------|
      | True Positive (TP)  | False Positive (FP)     |
      | False Negative (FN) | True Negative (TN)      |

      Example:
      TP = 80 (correctly found relevant info)
      FP = 20 (wrongly marked irrelevant info as relevant)
      FN = 10 (missed relevant info)
      TN = 90 (correctly ignored irrelevant info)

      Total samples = 80 + 20 + 10 + 90 = 200
    

Precision vs Recall tradeoff with examples

If the agent focuses on high precision, it means it only gives answers when very sure. This reduces wrong answers but might miss some useful info (lower recall). For example, a medical research assistant should avoid false info, so high precision is important.

If the agent focuses on high recall, it tries to find all possible relevant info, even if some are wrong (lower precision). This is good when missing any info is risky, like in legal research where missing a law could cause problems.

Balancing precision and recall depends on the research goal.

What good vs bad metric values look like

Good metrics: Precision and recall both above 0.8 means the agent finds most relevant info and makes few mistakes.

Bad metrics: Precision below 0.5 means many wrong answers. Recall below 0.5 means the agent misses too much important info.

For example, precision=0.9 and recall=0.85 is good. Precision=0.4 and recall=0.3 is bad.

Common pitfalls in metrics for Research assistant agent

• Accuracy paradox: If most info is irrelevant, a model that always says "not relevant" can have high accuracy but be useless.
• Data leakage: If the agent sees answers during training that appear in testing, metrics look better but real performance is worse.
• Overfitting: The agent may memorize specific documents and score high on test data but fail on new topics.

Self-check question

Your research assistant agent has 98% accuracy but only 12% recall on finding relevant documents. Is it good for production? Why or why not?

Answer: No, it is not good. The high accuracy is misleading because most documents are irrelevant, so the agent mostly says "not relevant". The very low recall means it misses almost all relevant documents, which defeats the purpose of a research assistant.

Key Result

Precision and recall are key to measure how well a research assistant agent finds relevant information accurately and completely.