Based on insights from Anthropic's Building Effective Agents, adapted in a simplified, beginner-friendly style.

Introduction

If you’re working with AI models like ChatGPT or Claude, you don’t always need to jump straight into building a complex agent. Start simple, then scale up as needed. This guide explains how to structure your AI app from the ground up, in a way that’s easy to understand and build.

Augmented LLMs

Think of this as your smart assistant. An augmented LLM is just a language model that can:

• Look up information (retrieval)

• Use tools like calculators or APIs

• Remember things (memory)

This makes it way more useful than just a basic chatbot.

Common LLM Workflows

These are step-by-step ways to make your app smarter. Let’s look at each one with simple examples.

1. Prompt Chaining

What it is: One step at a time. Each step uses the result from the last.

When to use: Your task can be split into clear steps.

Example:

• Step 1: Write a blog outline

• Step 2: Check if the outline is good

• Step 3: Write full blog using the outline

2. Routing

What it is: Choose the right path based on the input.

When to use: You have different types of tasks.

Example:

• A customer service bot: if it's a refund question, go to refund handler; if it's tech support, go there.

  

3. Parallelization

What it is: Do many things at the same time, then combine results.

When to use: You want to be fast, or get multiple opinions.

Example (Sectioning):

• One LLM checks if content is safe

• Another writes the actual response

Example (Voting):

• 3 LLMs give answers

• Pick the best one

4. Orchestrator-Workers

What it is: One smart LLM decides the steps, and other LLMs do the work.

When to use: You don’t know what steps are needed until you see the task.

Example:

• The main LLM sees the task: "Fix this bug."

• It says: "Edit file A, B, and C."

• Each worker edits one file.

5. Evaluator-Optimizer

What it is: One LLM writes something, another gives feedback, then it improves.

When to use: You want better quality with feedback loops.

Example:

• LLM writes a story

• Another LLM says: "The ending is weak"

• LLM rewrites the ending

When to Use Agents

An agent is like an LLM that can run on its own:

• Plans what to do

• Chooses the right tool

• Changes the plan if something fails

• Might ask for help if needed

Use agents when:

• You don’t know how many steps it will take

• You want the AI to figure things out on its own

Examples:

• Code agents that read an issue and fix the right files

• A research assistant that browses, reads, and summarizes

Tools & Frameworks

You can build all of this using code. But some tools can help:

• LangGraph (advanced flow with agents)

• Amazon Bedrock Agents (AWS service)

• Rivet and Vellum (drag-and-drop builders)

Tip: Start simple with just API calls. Only use frameworks when needed.

Final Tips

• Start with simple LLM prompts

• Use chaining or routing for structure

• Go for agents when the task is complex and flexible

• Always test and monitor agents before putting them into real-world use

Build smart. Start small. Scale only when you need to.