AI Product Engineer Day 1

Day 1: What are AI Agents?

Welcome to Day 1 of the AI Product Engineer Course, where we go beyond the theory and get hands-on with the next generation of AI systems.

AI agents aren’t just a buzzword; they’re quickly becoming the foundation of modern business operations. According to AI Agents Statistics:  85% of enterprises are projected to use AI agents this year.

What’s driving the surge? Advances in automation, more powerful AI models, and deep integration into enterprise workflows and consumer products. In short, agents are no longer optional; they’re becoming commonplace.

If you are planning to join the next AI Engineering Bootcamp on June 2nd, you must complete this 14-day course first.

14-Day Course Outline

Module 1: Foundations of Agentic Systems

• Day 1: What Are AI Agents?

  Understand what agents are, how they work, and why they’re transforming AI.

• Day 2: Agents vs Workflows

  Learn the architectural differences and when to use each.

• Day 3: RAG & Tool Use

  Make your agents smarter and more useful with retrieval and tool execution.

• Day 4: Memory – Teaching Agents to Remember

  Explore short-term and long-term memory strategies for personalized, context-aware agents.

• Day 5: Guardrails & Tracing

  Build safer, more reliable agents with validation, monitoring, and observability tools.

• Day 6: Prompt Engineering

  Master the art of structuring prompts to guide agent behavior and reasoning.

• Day 7: Tool-Calling & Orchestration

  Coordinate tools, models, and workflows to create robust, multi-step systems.

Module 2: Software Engineering Fundamentals

• Day 8: GitHub Dev Setup

  Set up your agent development environment using GitHub.

• Day 9: Cursor & Practice

  Learn and practice building agents in Cursor with real-world tasks.

• Day 10: Deploying with Vercel

  Deploy your agent as a web app using Vercel’s serverless infrastructure.

• Day 11: Model Pricing

  Understand token usage, cost estimation, and how to build budget-aware agents.

• Day 12: Web App Fundamentals

  Cover the basics of building interactive, agent-powered web applications.

• Day 13: TypeScript Crash Course

  Learn enough TypeScript to confidently build and debug your agent frontends.

• Day 14: APIs & Web Communication

  Connect agents to external data and services via APIs and HTTP requests.

Today, we’re diving into the core question: What exactly is an AI agent? And more importantly, why should you care?

The Agent Boom Is Real

Let’s talk about why now, the numbers say it all.

The global Agentic AI market is set to grow from $5.2B in 2024 to $196.6B by 2034 (CAGR: 43.8%). 🤯

What’s driving this?

• Better Reasoning Models: In 2025, we can let agents make complex decisions for us with the latest reasoning models.

• Ready-to-Deploy Agents: Plug-and-play and no-code tools for fast adoption.

• Build-Your-Own Agents Frameworks: More custom agents for devs and orgs with unique needs.

In a few years, agents won’t just assist, they’ll run operations, manage workflows, and enhance business processes across all industries.

Whether you’re building tools or exploring the space, now’s the best time to learn.

Real-World Use Cases

In 2025, we can see a wide adoption of AI Agents in vertical industries, like in the table below:

Now that you’ve seen what agents can do in the real world. from customer support to robotics, let’s talk about what they are and how to build them.

So what are the agents under the hood?

Agents are characterized by the following traits:

• Planning: Agents can break down tasks into smaller goals. Think: Chain-of-Thought reasoning.

• Memory: Short-term (in prompt) + long-term (external store) for context management.

• Tool Use: Connects to APIs, code interpreters, search, calculators, etc.

• Execution: These agents execute tasks by breaking them into smaller, manageable steps, iterating as needed.

Together, these make agents flexible and capable of complex, real-time decision-making.

Defining Agents

Before we dive into the details of how agents work, let’s first unpack this loaded and often misused term.

There are 2 common ways of defining the term “agent”.

• Some see agents as fully autonomous systems that independently complete complex tasks using a range of tools.

• Others use the term for more structured systems that follow predefined workflows.

If you want to be considered seriously in the engineering world, you shouldn't call them “Agents”. A better term would be “Agentic systems”.

Anthropic offers a helpful breakdown:

“Workflows follow fixed steps, with tools orchestrated by code.

Agents decide how to use tools and act dynamically to hit goals.”

In short: If it follows a script, it’s a workflow. If it figures it out on the fly, it’s an agent. But both of them considered agentic systems.

What are common challenges with agentic systems?

Contrary to popular belief, we are still in the early days of agentic systems. Most of the agentic systems currently deployed in production are workflows that have limited autonomy.

Why?

Making agents work reliably and consistently in production is extremely challenging.

And here’s the core problem: LLMs often fail, not because they’re bad models, but because it is hard to manage context.

Things like:

• Weak system prompts

• Vague user questions

• Missing or messy state updates

• Unclear tool descriptions

• etc

So if you want agents that don’t collapse under pressure, focus on this: Is your LLM getting the right context, at every step?

And this is what we teach in-depth at our AI Engineering Bootcamp. 

What is an agent framework?

An agent framework is a set of tools and abstractions that help you design, build, and manage AI agents. It takes care of the logic setup of the agent class with parameters like tools, context, and handoffs, etc.

However, when choosing the right framework, you need to consider the following:

Some frameworks are easy to start with, but you hit a wall fast.
Others let you build powerful systems, but only if you already know what you're doing.

Here’s how I think about it:

🔹 Low floor = Easy to get started, even for beginners
🔹 High floor = Takes serious experience just to get going

🔹 Low ceiling = You’ll outgrow it quickly
🔹 High ceiling = You can build complex, production-grade systems

Here is an example of the most common Python Agent frameworks on the graph above.

For this 14-Day Course, we’ll be using the OpenAI SDK for all Python examples, so you can follow along and start building immediately.

Congratulations!

You’ve just taken the first step into the world of AI agents,and things are only going to get more exciting from here.

Tomorrow, we’ll unpack one of the most important distinctions in modern AI design: Agents vs. Workflows. You’ll learn when to use each, how to spot the difference, and why it matters to know each.

PSA! This 14-Day Free Course is a prerequisite for the upcoming AI Product Engineering Bootcamp that starts on June 2nd!

Use an Early Access Coupon Code “EARLY” for $210 OFF!