AI vs Agentic AI

Introduction

Classic AI answers questions; Agentic AI achieves goals. The shift is from information retrieval to task completion. This single change cascades through architecture, evaluation, cost and risk.

Beginner analogy: a calculator (classic) tells you 2+2=4. An accountant (agent) takes your receipts, decides what to add, files your taxes and emails you a summary.

Understanding the topic

Core concepts:

• Classic: single prompt → single reply, stateless.
• Agentic: goal → many LLM calls + tool calls + memory.
• Classic AI is deterministic in shape; agents are open-ended.
• Agents need observability, classic LLMs need only a response.
• Agents fail in new ways: infinite loops, wrong tool, hallucinated args.

Syntax reference

Visual workflow / architecture:

┌─────────────────────┬─────────────────────┐
│   Classic LLM       │   Agentic AI        │
├─────────────────────┼─────────────────────┤
│ 1 prompt → 1 reply  │ goal → N steps      │
│ Stateless           │ Memory + state      │
│ No tools            │ Tools / APIs / code │
│ Predictable cost    │ Variable cost       │
│ Easy to eval        │ Needs trace eval    │
│ ChatGPT bubble      │ Devin, Operator     │
└─────────────────────┴─────────────────────┘

Real-world use

Customer support: a classic chatbot answers FAQs. An agentic system reads the ticket, queries the order DB, refunds the customer, updates Zendesk and writes the resolution note — autonomously.

Best practices

• Use classic LLM calls when the task is one shot.
• Use agents only when the task truly requires multi-step reasoning + tools.
• Hybrid: most production systems mix both.

Common mistakes

• Building an agent for a task that is just one prompt away.
• Underestimating cost — agents can call the LLM 20-50 times per run.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Three architectural differences between LLM apps and agent apps.
• Q2. When would you NOT use an agent?
• Q3. Why is evaluation harder for agents?
• Q4. How does cost scale differently for agents?
• Q5. Scenario: when would a hybrid approach win?