Why evaluation ensures agent reliability in Agentic AI - Experiment to Prove It

Experiment - Why evaluation ensures agent reliability

Problem:We have an AI agent designed to perform tasks automatically. However, we do not know if it always makes good decisions or if it sometimes fails.

Current Metrics:Agent success rate on test tasks: 60%. Agent failure rate: 40%.

Issue:The agent is unreliable because it fails too often. We need to evaluate it better to understand and improve its reliability.

Your Task

Improve the agent's reliability by using evaluation methods that measure its performance accurately and help identify weak points.

You can only change the evaluation process and feedback loop, not the agent's internal code.

You must keep the evaluation simple and easy to understand.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

Agentic AI

import random

class SimpleAgent:
    def perform_task(self, task):
        # Simulate agent success with 60% chance
        return random.random() < 0.6

class Evaluator:
    def __init__(self, agent, tasks):
        self.agent = agent
        self.tasks = tasks

    def evaluate(self):
        results = []
        for task in self.tasks:
            success = self.agent.perform_task(task)
            results.append(success)
        success_rate = sum(results) / len(results) * 100
        return success_rate

# Define tasks
tasks = ['task1', 'task2', 'task3', 'task4', 'task5', 'task6', 'task7', 'task8', 'task9', 'task10']

# Create agent and evaluator
agent = SimpleAgent()
evaluator = Evaluator(agent, tasks)

# Evaluate agent
initial_success_rate = evaluator.evaluate()

# Feedback loop: Identify failure tasks and retrain (simulate improvement)
# For simplicity, assume retraining improves success chance to 80%
class ImprovedAgent(SimpleAgent):
    def perform_task(self, task):
        return random.random() < 0.8

improved_agent = ImprovedAgent()
improved_evaluator = Evaluator(improved_agent, tasks)

improved_success_rate = improved_evaluator.evaluate()

print(f"Initial success rate: {initial_success_rate:.1f}%")
print(f"Improved success rate: {improved_success_rate:.1f}%")

Added an evaluation class to measure agent success rate on multiple tasks.

Created a feedback loop simulation by improving agent success probability after evaluation.

Used clear success rate metric to quantify reliability.

Results Interpretation

Before evaluation, the agent succeeded about 60% of the time, which is not very reliable.

After using evaluation to identify weaknesses and simulate improvement, the agent's success rate increased to about 80%, showing better reliability.

Evaluation helps us measure how well an agent performs. By knowing where it fails, we can improve it. This process makes the agent more reliable and trustworthy.

Bonus Experiment

Try evaluating the agent on different types of tasks with varying difficulty levels to see how reliability changes.

💡 Hint

Create tasks with easy and hard labels, then measure success rates separately to find which tasks need more improvement.