Decision Making in AI Agents: From Rules to Intelligence

Hey folks! 👋 Today we’re diving deep into how AI agents make decisions. We’re going way beyond simple if-else statements and building something that can actually think through problems.

This is the 5th article in the 30-day AI Agents from Zero to Hero series. Check out the first 3 articles if you haven’t already.

Previous Articles: ( Day 1, Day 2, Day 3, Day 4)

What We’re Building

We’re creating a decision-making system that can:

• Evaluate multiple options
• Learn from experience
• Handle uncertainty
• Adapt to new situations

Let’s get coding!

Starting Simple: Rule-Based Decision Making

First, let’s look at a basic rule-based system to understand the foundations:

from enum import Enum
from typing import Dict, List, Any, Tuple
import random
from dataclasses import dataclass

class Action(Enum):
    MOVE_LEFT = "left"
    MOVE_RIGHT = "right"
    MOVE_UP = "up"
    MOVE_DOWN = "down"
    WAIT = "wait"

@dataclass
class State:
    position: Tuple[int, int]
    energy: float
    has_goal: bool
    obstacles: List[Tuple[int, int]]

class SimpleDecisionMaker:
    def decide(self, state: State) -> Action:
        # Avoid obstacles
        for obstacle in state.obstacles:
            if…