A Beginner’s Guide to Agentic AI

As artificial intelligence continues to evolve, one buzzword is becoming more important: Agentic AI. You may have seen it in research papers, hackathons, or even product demos. But what does it really mean? Is it just marketing hype, or is there something truly new? In this post, we’ll explore what Agentic AI is, how it differs from traditional systems, and how it works through two key approaches: workflows and agents.

What Exactly is Agentic AI?

Even the experts can’t agree on a single definition for Agentic AI. The word “agent” gets used in so many ways that it can be confusing. But a simple and practical definition from smolagents is that “An AI agent is a system where the output of a language model (LLM) controls what happens next.” In short, instead of just answering questions, the LLM in an agentic system decides what to do, when to do it, and how to continue. To make it more clear, Traditional AI answers questions, but Agentic AI decides what to do next.

An AI system may be considered agentic if it demonstrates the ability to make multiple LLM calls, interact with external tools, communicate between LLMs, and exhibit planning and autonomy in completing tasks.

Approaches to Agentic Systems

Anthropic classifies the agentic systems into two categories:

• Workflows: Step-by-step processes that are planned out ahead of time
• Agents: Systems where the AI system figures out what to do as it goes

Both are considered agentic, but they work very differently.

Workflow-Based Agentic Systems

These systems are structured, predictable, and easy to monitor. They’re great when you know exactly how a task should be done. Let’s look at five useful workflow patterns:

1. Prompt Chaining

Prompt chaining is a technique where a complex task is broken down into a series of smaller, more manageable steps, each handled by a different prompt. Generating a marketing copy, then refining it for tone and clarity, and finally translating it into another language; each step handled by a different prompt; can be a perfect example of Prompt Chaining

Visual Representation

2. Routing

Routing workflow classifies an input and directs it to a specialized follow-up task. A customer care chatbot directing different types of customer service queries (general questions, refund requests, technical support) into different downstream processes, prompts, and tools can be a perfect example.

Visual Representation

3. Parallelization

LLMs can work in parallel, and their outputs can be combined using code, a technique called parallelization. It has two types: Sectioning, where a task is split into parts (e.g., extracting names, dates, and organizations from a resume separately), and Voting, where the same task is run multiple times and the best output is chosen (e.g., picking the best product description from multiple responses). This improves both speed and quality.

Visual Representation

4. Orchestrator-Worker

An orchestrator LLM (leader) dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results. The key difference from parallelization is its flexibility as the subtasks aren’t pre-defined and task breakdown is controlled by the LLM.

Visual Representation

5. Evaluator-Optimizer

In the evaluator-optimizer workflow, one LLM call generates a response while another provides evaluation and feedback in a loop. Writing an email draft using one LLM and having another LLM review it for tone and clarity—then refining the draft based on that feedback—is a perfect example of the evaluator-optimizer workflow

Visual Representation

Workflows are great for fixed tasks, but they struggle with change or uncertainty. That’s where agents step in—more flexible, adaptive, and capable of deciding what to do next.

Agent-Based Systems

Agents are more like improvisers. They observe what’s happening, think about what to do next, take action, see what happens, and then decide their next move. They follow a simple loop: Sense → Think → Act → Repeat. The Agent keeps going until it decides the job is done. Even though agents are flexible, they often follow patterns like ReAct (think–act–reflect), Tool Use (deciding when to use tools), CodeAct (writing and running code), Self-Reflection (reviewing and improving their own work), and Multi-Agent Collaboration (agents working together with roles like planner or checker). An AI agent that searches for budget video editing laptops, compares specs, asks follow-up questions, and adapts its steps based on your answers is a perfect example of flexible, plan-as-you-go agent behavior.

Visual Representation

Agents are flexible and adaptive, but they can be unpredictable, costly, and hard to debug—so they need monitoring and guardrailing.

Final Thoughts

Agentic AI is about more than just smart answers—it’s about AI that can act, adapt, and make decisions. Workflows offer structure, while agents add flexibility, and both have their roles.
The future of AI lies in finding the right balance between control and autonomy.

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