Introduction to Agentic AI

Duration: 5 min

This module provides an introduction to Agentic AI, focusing on key patterns such as planning, reflection, tool use, and frameworks like CrewAI and AutoGen. Understanding these concepts is crucial for developing sophisticated AI systems that can operate autonomously and efficiently.

Planning in Agentic AI

Planning in Agentic AI involves the creation of a sequence of actions that an agent will take to achieve a specific goal. This process requires the agent to evaluate different possible actions and their outcomes, selecting the most optimal path. Effective planning allows agents to navigate complex environments and make decisions that maximize their performance and efficiency.

import random

# Simple planning example
def plan_actions():
    actions = ['move_forward', 'turn_left', 'turn_right', 'pick_up_object']
    plan = [random.choice(actions) for _ in range(5)]
    return plan

# Execute the plan
plan = plan_actions()
print(plan)

Try it in Google Colab:

['turn_left','move_forward', 'pick_up_object', 'turn_right','move_forward']

Reflection in Agentic AI

Reflection in Agentic AI is the process by which an agent evaluates its past actions and decisions to improve future performance. This involves analyzing the outcomes of actions, identifying what worked and what didn't, and adjusting strategies accordingly. Reflection enables agents to learn from experience and adapt to changing conditions.

def reflect_on_actions(actions, outcomes):
    reflection = {}
    for action, outcome in zip(actions, outcomes):
        if outcome =='success':
            reflection[action] = 'effective'
        else:
            reflection[action] = 'ineffective'
    return reflection

# Example actions and outcomes
actions = ['move_forward', 'turn_left', 'pick_up_object']
outcomes = ['success', 'failure','success']
reflection = reflect_on_actions(actions, outcomes)
print(reflection)

💡 Tip: Ensure that the reflection process includes both successful and unsuccessful actions to provide a comprehensive learning experience for the agent.