Why agents make autonomous decisions in Prompt Engineering / GenAI - Experiment to Prove It

Experiment - Why agents make autonomous decisions

Problem:We want to understand why AI agents make decisions on their own without human help. Currently, the agent acts autonomously but we do not know how changing its decision-making rules affects its behavior.

Current Metrics:Agent completes tasks with 80% success rate but sometimes makes unexpected choices.

Issue:The agent's decision process is not transparent, and it occasionally makes poor decisions that reduce overall success.

Your Task

Improve the agent's decision-making so it makes better autonomous choices, increasing task success rate to over 90%.

You cannot remove the agent's autonomy.

You must keep the agent's ability to learn from its environment.

Hint 1

Hint 2

Hint 3

Solution

Prompt Engineering / GenAI

import random

class AutonomousAgent:
    def __init__(self, threshold=0.5):
        self.threshold = threshold
        self.success_count = 0
        self.total_trials = 0

    def decide(self, state_value):
        # Agent decides to act if state_value exceeds threshold
        return state_value > self.threshold

    def act(self, state_value):
        decision = self.decide(state_value)
        # Simulate success if decision matches environment condition
        success = decision == (state_value > 0.6)
        self.total_trials += 1
        if success:
            self.success_count += 1
        return success

    def success_rate(self):
        return self.success_count / self.total_trials if self.total_trials else 0

# Simulate agent behavior
agent = AutonomousAgent(threshold=0.55)
for _ in range(1000):
    state = random.random()  # Random state between 0 and 1
    agent.act(state)

print(f"Success rate: {agent.success_rate() * 100:.2f}%")

Added a decision threshold to control when the agent acts.

Set threshold to 0.55 to reduce risky decisions.

Simulated environment condition to compare agent's decision for success.

Results Interpretation

Before: Agent success rate was 80%, with some poor decisions.

After: Success rate increased to about 95% by adding a decision threshold.

This shows that tuning decision rules helps autonomous agents make better choices, improving their performance while keeping independence.

Bonus Experiment

Try adding a learning mechanism where the agent adjusts its threshold based on past success.

💡 Hint

Use a simple feedback loop to increase threshold if success rate drops, or decrease if success rate is high.