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AI Agents

AI Agents are systems that go beyond generating text. Instead of only predicting the next token, they take actions, make decisions, and complete tasks step by step to achieve a goal.

This is the key shift:

  • Generative AI → predicts next word
  • AI Agents → decide next action

Generative AI vs AI Agents

Generative AI (Next Token Prediction)

Generative AI models (LLMs) work by predicting the next token in a sequence.

Example:

Input: "The capital of France is"
Output: "Paris"

How it works:

  1. Break input into tokens
  2. Predict probability of next token
  3. Select most likely token
  4. Repeat until response is complete

Limitations:

  • No real goal awareness
  • Cannot take actions
  • Stateless (each prompt is isolated)
  • Can hallucinate

AI Agents (Step-by-Step Decision Making)

AI Agents operate in a loop where they:

  1. Understand a goal
  2. Plan steps
  3. Take actions (tools/APIs)
  4. Observe results
  5. Adjust and repeat

Example goal:

"Find the best laptop under $1000 and summarize top 3 options"

Agent behavior:

  • Search web
  • Compare products
  • Summarize results
  • Deliver final answer

Core Difference

Aspect Generative AI AI Agents
Core Function Next token prediction Action selection
Goal Awareness No Yes
Memory Limited Can maintain state
Tool Usage No Yes
Reliability Lower Higher (with iteration)

Agent Loop

AI Agents follow a reasoning loop:

Goal → Plan → Act → Observe → Reflect → Repeat

This loop allows agents to improve results over time instead of generating a single response.

How Agents Use LLMs

Important:

Agents still use LLMs internally.

But instead of using them once, they use them repeatedly to:

  • Decide next step
  • Choose tools
  • Evaluate results

LLM becomes the brain, agent becomes the system.

Example: Simple Agent

def agent(task):
    if "weather" in task:
        return "Calling weather API..."
    elif "news" in task:
        return "Fetching latest news..."
    else:
        return "Planning next step..."

print(agent("weather today"))

Real-World Use Cases

  • Research assistants
  • Coding agents
  • Workflow automation
  • Customer support automation
  • Data analysis pipelines

Why Agents Are More Reliable

Generative AI:

  • Produces one-shot answers
  • No verification
  • Can hallucinate

Agents:

  • Break problems into steps
  • Validate intermediate results
  • Retry if needed
  • Use external data sources

This makes agents better for real-world applications.

Agent + RAG + Tools

Modern agents combine multiple systems:

id="p9d3sa" User Request ↓ Agent ↓ LLM (reasoning) ↓ RAG (knowledge retrieval) ↓ Tools / APIs ↓ Final Output

Types of Agents

  • Reactive Agents → respond immediately
  • Planning Agents → create multi-step plans
  • Tool-Using Agents → interact with APIs
  • Autonomous Agents → operate independently

Common Frameworks

  • LangChain Agents
  • AutoGen
  • CrewAI
  • Semantic Kernel