Agent perception-reasoning-action loop in Agentic AI - ML Experiment: Train & Evaluate

Experiment - Agent perception-reasoning-action loop

Problem:Create an agent that perceives its environment, reasons about what to do, and takes actions accordingly. The current agent model reacts randomly without learning from perception, resulting in poor task completion.

Current Metrics:Task completion rate: 40%, Average decision time: 0.1s

Issue:The agent does not use perception data effectively and chooses actions randomly, leading to low task success.

Your Task

Improve the agent's reasoning so it uses perception data to select better actions, increasing task completion rate to at least 75% while keeping decision time under 0.5s.

Do not change the environment or task complexity.

Keep the agent's action set fixed.

Use only perception and reasoning improvements.

Hint 1

Hint 2

Hint 3

Solution

Agentic AI

import random

class Agent:
    def __init__(self):
        pass

    def perceive(self, environment):
        # Perceive environment state (e.g., obstacle presence, goal direction)
        perception = {
            'obstacle_ahead': environment.get('obstacle_ahead', False),
            'goal_direction': environment.get('goal_direction', 'forward')
        }
        return perception

    def reason(self, perception):
        # Simple rules to decide action based on perception
        if perception['obstacle_ahead']:
            action = 'turn_left'
        elif perception['goal_direction'] == 'forward':
            action = 'move_forward'
        else:
            action = 'turn_right'
        return action

    def act(self, action):
        # Perform the chosen action
        return f'Action taken: {action}'

# Simulate environment and agent interaction
agent = Agent()
environments = [
    {'obstacle_ahead': True, 'goal_direction': 'forward'},
    {'obstacle_ahead': False, 'goal_direction': 'forward'},
    {'obstacle_ahead': False, 'goal_direction': 'right'},
    {'obstacle_ahead': True, 'goal_direction': 'right'},
    {'obstacle_ahead': False, 'goal_direction': 'forward'}
]

success_count = 0
for env in environments:
    perception = agent.perceive(env)
    action = agent.reason(perception)
    result = agent.act(action)
    # Assume success if action matches expected behavior
    expected_action = 'turn_left' if env['obstacle_ahead'] else ('move_forward' if env['goal_direction'] == 'forward' else 'turn_right')
    if action == expected_action:
        success_count += 1

completion_rate = (success_count / len(environments)) * 100
print(f'Task completion rate: {completion_rate}%')

Added a perception method to extract environment state.

Implemented a reasoning method with simple rules mapping perception to actions.

Replaced random action selection with rule-based action choice.

Kept action method to perform chosen action.

Results Interpretation

Before: Task completion rate was 40% with random actions.

After: Task completion rate improved to 100% using perception and reasoning rules.

Using perception data with simple reasoning rules helps an agent choose better actions, improving task success without complex learning.

Bonus Experiment

Try adding a memory component so the agent remembers previous perceptions to avoid repeating mistakes.

💡 Hint

Store recent perceptions and actions in a list and adjust reasoning to consider past states.

Practice

1. What is the correct order of steps in the agent perception-reasoning-action loop?

easy

A. Act, Reason, Perceive

B. Act, Perceive, Reason

C. Reason, Act, Perceive

D. Perceive, Reason, Act

Solution

1. Step 1: Understand the agent loop components

   The agent loop consists of three main steps: perceiving the environment, reasoning about the information, and then acting based on that reasoning.

2. Step 2: Identify the correct sequence

   The agent must first perceive to gather data, then reason to decide what to do, and finally act to affect the environment.

3. Final Answer:

   Perceive, Reason, Act -> Option D

4. Quick Check:

   Agent loop order = Perceive, Reason, Act [OK]

Hint: Remember: see first, think second, do last [OK]

Common Mistakes:

• Mixing up the order of reasoning and acting
• Thinking action happens before perception
• Skipping the reasoning step

2. Which of the following code snippets correctly represents the agent loop structure in Python?

easy

A. while True:
reason()
act()
perceive()

B. while True:
act()
perceive()
reason()

C. while True:
perceive()
reason()
act()

D. while True:
act()
reason()
perceive()

Solution

1. Step 1: Check the order of function calls

   The agent loop must call perceive() first, then reason(), then act() inside the loop.

2. Step 2: Verify the code snippet matches this order

   while True: perceive() reason() act() calls perceive(), then reason(), then act(), which matches the correct loop order.

3. Final Answer:

   while True:\n perceive()\n reason()\n act() -> Option C

4. Quick Check:

   Code order = perceive, reason, act [OK]

Hint: Loop order matches perception, reasoning, then action [OK]

Common Mistakes:

• Calling act() before perceive()
• Swapping reason() and act() calls
• Incorrect indentation causing syntax errors

3. Given this simplified agent loop code, what will be printed?

def perceive():
    return "data"
def reason(data):
    return data.upper()
def act(result):
    print(f"Action: {result}")

for _ in range(2):
    data = perceive()
    result = reason(data)
    act(result)

medium

A. Action: DATA\nAction: DATA

B. Error: missing argument in reason()

C. Action: Data\nAction: Data

D. Action: data\nAction: data

Solution

1. Step 1: Trace the function calls in the loop

   Each loop iteration calls perceive() returning "data", then reason(data) converts it to uppercase "DATA", then act(result) prints "Action: DATA".

2. Step 2: Repeat for two iterations

   The loop runs twice, so the print happens twice with "Action: DATA" each time.

3. Final Answer:

   Action: DATA\nAction: DATA -> Option A

4. Quick Check:

   Uppercase output printed twice = Action: DATA [OK]

Hint: Check function returns and loop count carefully [OK]

Common Mistakes:

• Assuming reason() returns original lowercase
• Forgetting to pass argument to reason()
• Confusing print output formatting

4. Identify the error in this agent loop code snippet:

def perceive():
    return "info"
def reason():
    # missing parameter
    return "processed"
def act(result):
    print(result)

while True:
    data = perceive()
    result = reason()
    act(result)
    break

medium

A. act() should not print the result

B. reason() should accept an argument but does not

C. perceive() should not return a value

D. while loop should not have a break

Solution

1. Step 1: Check function parameters and calls

   perceive() returns "info" which is stored in data, but reason() is called without arguments though it should process data.

2. Step 2: Identify mismatch causing error

   reason() lacks a parameter to receive data, so calling reason() without argument causes a logic error or mismatch.

3. Final Answer:

   reason() should accept an argument but does not -> Option B

4. Quick Check:

   Function parameter mismatch = reason() missing argument [OK]

Hint: Match function parameters with calls exactly [OK]

Common Mistakes:

• Ignoring missing parameter in reason()
• Thinking perceive() should not return data
• Assuming break is incorrect in loop

5. You want to design an agent that perceives temperature, reasons if it's too hot or cold, and acts by turning on a heater or cooler. Which code snippet correctly implements this agent loop?

hard

A. def perceive():
return 30

def reason(temp):
if temp > 25:
return "cooler"
elif temp < 18:
return "heater"
else:
return "off"

def act(action):
print(f"Turn {action} on")

while True:
temp = perceive()
action = reason(temp)
act(action)
break

B. def perceive():
return 30

def reason():
if temp > 25:
return "cooler"
elif temp < 18:
return "heater"
else:
return "off"

def act(action):
print(f"Turn {action} on")

while True:
temp = perceive()
action = reason()
act(action)
break

C. def perceive():
return 30

def reason(temp):
if temp < 18:
return "cooler"
elif temp > 25:
return "heater"
else:
return "off"

def act(action):
print(f"Turn {action} on")

while True:
temp = perceive()
action = reason(temp)
act(action)
break

D. def perceive():
return 30

def reason(temp):
if temp > 25:
return "heater"
elif temp < 18:
return "cooler"
else:
return "off"

def act(action):
print(f"Turn {action} on")

while True:
temp = perceive()
action = reason(temp)
act(action)
break

Solution

1. Step 1: Check perception and reasoning logic

   perceive() returns temperature 30. reason(temp) correctly returns "cooler" if temp > 25, "heater" if temp < 18, else "off".

2. Step 2: Verify action and loop structure

   act(action) prints the correct command. The loop calls perceive(), reason(temp), and act(action) in correct order and breaks after one iteration.

3. Final Answer:

   Option A correctly implements the agent loop with proper logic and function calls -> Option A

4. Quick Check:

   Correct logic and loop = def perceive(): return 30 def reason(temp): if temp > 25: return "cooler" elif temp < 18: return "heater" else: return "off" def act(action): print(f"Turn {action} on") while True: temp = perceive() action = reason(temp) act(action) break [OK]

Hint: Match temperature conditions with correct actions [OK]

Common Mistakes:

• Missing parameter in reason() function
• Swapping heater and cooler logic
• Calling reason() without argument