Self-improving agents in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a self-improving agent class with an update method.

Agentic AI

class SelfImprovingAgent:
    def __init__(self, model):
        self.model = model

    def [1](self, data):
        # Improve the model using new data
        self.model.train(data)

Drag options to blanks, or click blank then click option'

Aevaluate

Bpredict

Cinitialize

Dupdate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' instead of 'update' because prediction does not improve the model.

Using 'initialize' which is for setup, not improvement.

The method to improve or refine the agent's model is typically called 'update'.

2fill in blank

medium

Complete the code to make the agent improve itself by retraining on its own predictions.

Agentic AI

def self_improve(agent, data):
    predictions = agent.predict(data)
    agent.[1](predictions)

Drag options to blanks, or click blank then click option'

Ainitialize

Breset

Cupdate

Devaluate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'initialize' which resets the model instead of improving it.

Using 'evaluate' which only measures performance.

The agent should update its model using the predictions to improve itself.

3fill in blank

hard

Fix the error in the code to correctly implement a self-improving loop.

Agentic AI

for epoch in range(10):
    predictions = agent.predict(data)
    agent.[1](predictions)
    loss = agent.evaluate(data)
    print(f"Epoch {epoch}: Loss = {loss}")

Drag options to blanks, or click blank then click option'

Aupdate

Bpredict

Ctrain

Devaluate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predict' which does not change the model.

Using 'evaluate' which only measures performance.

The agent should update its model with predictions to improve, not predict again.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps data points to their improved predictions.

Agentic AI

improved_predictions = {point: agent.[1](point) for point in data if agent.[2](point) > 0.5}

Drag options to blanks, or click blank then click option'

Apredict

Bevaluate

Cconfidence_score

Dupdate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'update' in the comprehension which changes the model instead of predicting.

Using 'evaluate' which returns overall loss, not per data point score.

We predict for each point and filter by confidence_score greater than 0.5.

5fill in blank

hard

Fill all three blanks to implement a self-improving agent loop that predicts, filters, and updates.

Agentic AI

for data_point in dataset:
    prediction = agent.[1](data_point)
    if prediction [2] 0.7:
        agent.[3]([data_point])

Drag options to blanks, or click blank then click option'

Apredict

B>

Cupdate

Devaluate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'evaluate' instead of 'update' to improve the model.

Using '<' instead of '>' which reverses the condition.

The agent predicts, checks if prediction is greater than 0.7, then updates with that data point.

Practice

1. What is the main idea behind a self-improving agent in AI?

easy

A. It learns from its own actions to get better over time.

B. It only follows fixed rules without changing.

C. It requires constant manual updates to improve.

D. It ignores feedback from the environment.

Solution

1. Step 1: Understand the agent's learning process

   A self-improving agent learns by trying actions and observing results to improve itself.

2. Step 2: Compare options to the definition

   Only It learns from its own actions to get better over time. describes learning from its own actions to improve over time.

3. Final Answer:

   It learns from its own actions to get better over time. -> Option A

4. Quick Check:

   Self-improving means learning from actions = B [OK]

Hint: Self-improving means learning and updating itself [OK]

Common Mistakes:

• Thinking it never changes (fixed rules)
• Assuming manual updates are needed
• Ignoring feedback from environment

2. Which of the following is the correct way to represent a self-improving agent's update step in pseudocode?

easy

A. agent.reset() every time without learning

B. agent.run() without feedback

C. agent.update(learn_from=agent.actions, feedback=environment.results)

D. agent.ignore(environment.results)

Solution

1. Step 1: Identify update step involving learning

   The agent must update itself using its actions and feedback from the environment.

2. Step 2: Match options to update logic

   Only agent.update(learn_from=agent.actions, feedback=environment.results) shows the agent updating by learning from its actions and feedback.

3. Final Answer:

   agent.update(learn_from=agent.actions, feedback=environment.results) -> Option C

4. Quick Check:

   Update with actions and feedback = A [OK]

Hint: Update means learning from actions and feedback [OK]

Common Mistakes:

• Ignoring feedback in update
• Resetting without learning
• Running without update

3. Consider this pseudocode for a self-improving agent:

actions = ['move', 'turn', 'scan']
results = [True, False, True]
agent_knowledge = {'move': 0.5, 'turn': 0.5, 'scan': 0.5}

for i in range(len(actions)):
    if results[i]:
        agent_knowledge[actions[i]] += 0.1
    else:
        agent_knowledge[actions[i]] -= 0.1

print(agent_knowledge)

What will be the printed output?

medium

A. SyntaxError

B. {'move': 0.6, 'turn': 0.4, 'scan': 0.6}

C. {'move': 0.4, 'turn': 0.6, 'scan': 0.4}

D. {'move': 0.5, 'turn': 0.5, 'scan': 0.5}

Solution

1. Step 1: Analyze loop updates on knowledge

   For each action, if result is True, add 0.1; if False, subtract 0.1.

2. Step 2: Calculate final values

   'move': 0.5 + 0.1 = 0.6; 'turn': 0.5 - 0.1 = 0.4; 'scan': 0.5 + 0.1 = 0.6.

3. Final Answer:

   {'move': 0.6, 'turn': 0.4, 'scan': 0.6} -> Option B

4. Quick Check:

   True adds 0.1, False subtracts 0.1 = D [OK]

Hint: Add 0.1 for True, subtract 0.1 for False in order [OK]

Common Mistakes:

• Not updating values correctly
• Mixing True and False effects
• Assuming no change

4. This code tries to update an agent's knowledge but has a bug:

actions = ['jump', 'run']
results = [True, False]
knowledge = {'jump': 0.3, 'run': 0.7}

for i in range(len(actions)):
    if results[i]:
        knowledge[actions[i]] += 0.1
    else:
        knowledge[actions[i]] =- 0.1

print(knowledge)

What is the bug and how to fix it?

medium

A. The operator '= -' should be '-=' to subtract; fix: change to '-='.

B. The list lengths mismatch; fix by adding more results.

C. The dictionary keys are missing; fix by adding keys.

D. The print statement is incorrect; fix by using print(knowledge.values()).

Solution

1. Step 1: Identify the incorrect operator

   The code uses '= - 0.1' which assigns negative 0.1 instead of subtracting.

2. Step 2: Correct the operator to '-='

   Changing '= -' to '-=' correctly subtracts 0.1 from the current value.

3. Final Answer:

   The operator '= -' should be '-=' to subtract; fix: change to '-='. -> Option A

4. Quick Check:

   Use '-=' to subtract, not '= -' = C [OK]

Hint: Use '-=' to subtract, not '= -' [OK]

Common Mistakes:

• Confusing '= -' with '-=' operator
• Ignoring operator syntax errors
• Thinking print statement causes error

5. You want to design a self-improving agent that adapts to changing environments by updating its strategy based on success rates. Which approach best fits this goal?

hard

A. Manually update the agent's strategy after every 100 actions.

B. Fix the agent's strategy and never update it to keep consistency.

C. Randomly change strategies without considering past results.

D. Use a feedback loop where the agent tries actions, measures success, and updates probabilities accordingly.

Solution

1. Step 1: Understand the goal of adapting strategies

   The agent must learn from success rates and update its strategy automatically.

2. Step 2: Evaluate options for self-improvement

   Only Use a feedback loop where the agent tries actions, measures success, and updates probabilities accordingly. describes a feedback loop that updates based on success, matching self-improving behavior.

3. Final Answer:

   Use a feedback loop where the agent tries actions, measures success, and updates probabilities accordingly. -> Option D

4. Quick Check:

   Feedback loop with updates = A [OK]

Hint: Use feedback loops to update strategy automatically [OK]

Common Mistakes:

• Fixing strategy without updates
• Changing randomly without feedback
• Relying on manual updates only