Self-improving agents in Agentic AI

Introduction

Self-improving agents learn from their own actions to get better over time. They help machines solve problems more efficiently without needing constant human help.

When a robot needs to adapt to new tasks without being reprogrammed.

When a virtual assistant improves its responses by learning from past conversations.

When a game AI learns new strategies by playing against itself.

When a recommendation system updates itself based on user feedback automatically.

When an autonomous car improves its driving by learning from its own experiences.

Syntax

Agentic AI

agent = SelfImprovingAgent(environment)
for episode in range(num_episodes):
    state = environment.reset()
    done = False
    while not done:
        action = agent.choose_action(state)
        next_state, reward, done = environment.step(action)
        agent.learn(state, action, reward, next_state)
        state = next_state

The agent interacts with an environment step-by-step.

It learns from the results of its actions to improve future decisions.

Examples

The agent learns by running 100 episodes in the environment.

Agentic AI

agent = SelfImprovingAgent(env)
agent.learn_from_experience(episodes=100)

The agent chooses an action, observes the result, and updates itself.

Agentic AI

action = agent.choose_action(current_state)
next_state, reward, done = env.step(action)
agent.learn(current_state, action, reward, next_state)

Sample Model

This simple program shows an agent learning to reach state 5 by moving +1 or -1. It updates its knowledge based on rewards and improves its choices over 10 episodes.

Agentic AI

import random

class SimpleEnvironment:
    def __init__(self):
        self.state = 0
    def reset(self):
        self.state = 0
        return self.state
    def step(self, action):
        # action: +1 or -1
        self.state += action
        reward = 1 if self.state == 5 else -1
        done = self.state == 5
        return self.state, reward, done

class SelfImprovingAgent:
    def __init__(self):
        self.actions = [-1, 1]
        self.knowledge = {i: 0 for i in range(-10, 11)}
    def choose_action(self, state):
        # Choose action with highest expected reward
        scores = {a: self.knowledge.get(state + a, 0) for a in self.actions}
        best_action = max(scores, key=scores.get)
        return best_action
    def learn(self, state, action, reward, next_state):
        # Update knowledge with reward
        self.knowledge[state] = self.knowledge.get(state, 0) + reward

# Run training
env = SimpleEnvironment()
agent = SelfImprovingAgent()

for episode in range(10):
    state = env.reset()
    done = False
    while not done:
        action = agent.choose_action(state)
        next_state, reward, done = env.step(action)
        agent.learn(state, action, reward, next_state)
        state = next_state

# Test agent after learning
state = env.reset()
done = False
actions_taken = []
while not done:
    action = agent.choose_action(state)
    actions_taken.append(action)
    state, reward, done = env.step(action)

print(f"Actions taken to reach goal: {actions_taken}")

Important Notes

Self-improving agents learn by trying actions and seeing what works best.

They get better with experience, like practicing a skill.

Designing the reward system carefully helps the agent learn the right behavior.

Summary

Self-improving agents learn from their own actions to improve over time.

They are useful when tasks or environments change and manual updates are hard.

Learning happens by trying, observing results, and updating knowledge.

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