Agentic AI

AI Agents vs Agentic AI: Key Differences

AI agents and agentic AI are related but distinct concepts in artificial intelligence, differing primarily in autonomy, complexity, adaptability, and operational scope[1][2][3][5].

Definition and Scope

• AI Agents
• Individual software entities designed to perform specific tasks within defined parameters or rules[2][3][5].
• Examples: Chatbots for customer service, automated scheduling assistants, or password reset bots[2][3].

• Typically reactive-they respond to inputs or triggers but do not proactively seek out new tasks or adapt to changing environments unless explicitly reprogrammed[2][3][5].

• Agentic AI

• A broader, more advanced system that exhibits autonomous decision-making, goal-driven behavior, learning, and adaptation[2][4][5].
• Orchestrates and coordinates multiple AI agents or subsystems to achieve complex, multi-step objectives[2][4].
• Proactively analyzes situations, plans actions, learns from feedback, and adapts strategies in real time[2][3][4][5].

Core Differences

| Feature | AI Agents | Agentic AI |
|————————–|——————————————-|————————————————-|
| Autonomy | Operate within predefined rules and tasks | High autonomy-can set goals and act independently[2][4][5] |
| Adaptability | Limited; often requires reprogramming | Learns and adapts from experience and feedback[2][5] |
| Scope | Task-oriented; narrow focus | Goal-oriented; manages complex, multi-agent workflows[2][5] |
| Proactiveness | Reactive; responds to triggers | Proactive; anticipates needs and takes initiative[2][3][5] |
| Learning | Static or slow to update | Continuously improves and refines behavior[2][5] |
| Complexity | Simple, rule-based | Handles multi-step, cross-domain processes[2][4][5] |
| Integration | Usually standalone or single-system | Integrates multiple agents, tools, and data sources[2][5] |

Practical Example

• AI Agent: A chatbot that answers FAQs using a fixed script.
• Agentic AI: An IT support system that understands user issues, coordinates multiple agents (ticketing, knowledge base, user communication), decides on escalation, and takes corrective actions-all while learning and improving over time[2][4].

Relationship

• Agentic AI is the overarching framework or system, while AI agents are the building blocks or components within that system[4][7].
• Agentic AI leverages multiple AI agents to achieve broader objectives, providing orchestration, autonomy, and adaptability beyond what any single agent can do[2][4][7].

Agentic AI Sample Usecases

• E-Commerce:
  • Logistics Agent: We can build goal based agents to ship the stuff faster for prime customers.
• Trading:
  • Swing Trading Agent: To buy/sell to minimize losses
• Hiring:
  • Hiring Agent (Premium): Try to filter a best profile around senior management roles
• Social media Posts:
  • Here we need Research, Generate, Review

Leyman’s Architecutural types in Agentic AI

• Single Agent
• Multi Agent

Frameworks for in Building Agents

• langgraph (lang-* family)
• CrewAI
• AutoGen

Agentic AI Possible Scenarios

• Educational Institution
• Hiring (HR)
• Customer Service
• IT Ticketing
• Personal Education
• Jobs:
  • Employer
  • Employee

Education Institution

• Exams, Results, PTM (Goal => Better Marks)