Plan-and-execute pattern in Agentic AI - Model Metrics & Evaluation

Metrics & Evaluation - Plan-and-execute pattern

Which metric matters for the Plan-and-execute pattern and WHY

The Plan-and-execute pattern involves an AI agent first creating a plan and then carrying it out. To evaluate this, we focus on task success rate and execution accuracy. Task success rate tells us if the agent completed the goal correctly. Execution accuracy measures how well the agent followed the plan steps. These metrics matter because a good plan is useless if not executed well, and good execution without a good plan may fail the goal.

Confusion matrix or equivalent visualization

Task Outcome Confusion Matrix:

                Predicted Success   Predicted Failure
Actual Success       TP = 85            FN = 15
Actual Failure       FP = 10            TN = 90

Total samples = 200

- TP (True Positive): Agent planned and executed successfully, and task succeeded.
- FP (False Positive): Agent thought task succeeded but it failed.
- FN (False Negative): Agent failed task despite planning and execution.
- TN (True Negative): Agent correctly identified failure or aborted.

From this:
- Precision = TP / (TP + FP) = 85 / (85 + 10) = 0.895
- Recall = TP / (TP + FN) = 85 / (85 + 15) = 0.85
- F1 Score = 2 * (0.895 * 0.85) / (0.895 + 0.85) ≈ 0.872
    

Precision vs Recall tradeoff with concrete examples

In plan-and-execute, precision means when the agent says it succeeded, it really did. High precision avoids false success claims, important in safety-critical tasks like robot surgery.

Recall means the agent finds all successful plans and executions. High recall ensures the agent does not miss opportunities to complete tasks, important in customer support bots that must solve all queries.

For example, a delivery robot with high precision but low recall might only deliver some packages but never claim false success. A robot with high recall but low precision might claim success often but sometimes fail deliveries, causing trust issues.

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

Good metrics: Precision and recall above 85% show the agent reliably plans and executes tasks correctly and reports success accurately.

Bad metrics: Precision below 70% means many false success claims, risking trust. Recall below 60% means many missed successful executions, reducing usefulness.

Also, a large gap between precision and recall indicates imbalance: either the agent is too cautious or too optimistic.

Common pitfalls in metrics for Plan-and-execute pattern

• Accuracy paradox: High overall accuracy can hide poor execution if most tasks are easy or fail by default.
• Data leakage: If the agent sees test tasks during training, metrics will be unrealistically high.
• Overfitting: Agent may memorize plans for training tasks but fail new ones, causing low recall.
• Ignoring execution errors: Only measuring plan quality without execution accuracy misses real-world failures.

Self-check question

Your plan-and-execute agent has 98% accuracy but only 12% recall on successful task completion. Is it good for production? Why or why not?

Answer: No, it is not good. The high accuracy likely comes from many failed tasks correctly identified, but the very low recall means the agent misses almost all successful executions. It fails to complete tasks reliably, so it is not useful in real situations.

Key Result

Task success rate and execution accuracy (precision and recall) are key to evaluate plan-and-execute agents effectively.