Sequential step execution in Agentic AI - Model Metrics & Evaluation

Metrics & Evaluation - Sequential step execution

Which metric matters for Sequential step execution and WHY

In sequential step execution, the key metric is accuracy of each step's output and the overall success rate of the entire sequence. This is because each step depends on the previous one, so an error early on can cause the whole process to fail.

Measuring step-wise accuracy helps identify where errors happen. Measuring sequence completion rate shows how often the full process succeeds.

Confusion matrix or equivalent visualization

Step 1: Correct (TP) / Incorrect (FP)
Step 2: Correct (TP) / Incorrect (FP)
...

Example for a 3-step sequence with 100 runs:

Step 1: TP=90, FP=10
Step 2: TP=85, FP=5 (only on 90 correct from step 1)
Step 3: TP=80, FP=5 (only on 85 correct from step 2)

Overall success = 80/100 = 80%
    

Precision vs Recall tradeoff with concrete examples

In sequential steps, precision means how many executed steps were correct out of all attempted steps.

Recall means how many correct steps were completed out of all steps that should have been done.

For example, in a multi-step task like booking a trip, high precision means the steps done are mostly right, but low recall means some steps are skipped or missed.

Balancing precision and recall ensures the sequence is both accurate and complete.

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

Good: Step accuracy above 90%, overall sequence success above 85%. This means most steps are done correctly and the full sequence completes well.

Bad: Step accuracy below 70%, sequence success below 60%. This means many errors happen and the sequence often fails.

Metrics pitfalls

• Ignoring step dependencies: Measuring only final output without checking each step can hide where errors occur.
• Overfitting to training sequences: Model may perform well on known sequences but fail on new ones.
• Data leakage: Using future step information to predict earlier steps inflates metrics falsely.
• Accuracy paradox: High overall accuracy may hide poor performance on critical steps.

Self-check question

Your model has 98% accuracy on individual steps but only 12% recall on the full sequence completion. Is it good for production? Why or why not?

Answer: No, it is not good. High step accuracy means steps done are mostly correct, but very low recall on full sequence means most sequences are incomplete or fail. This shows the model misses many steps or fails to execute the full sequence reliably, which is critical for sequential tasks.

Key Result

Step-wise accuracy and overall sequence success rate are key to evaluate sequential step execution effectively.