Introduction to Agentic Design Patterns

Imagine an AI not as a passive tool that simply waits for instructions, but rather as a proactive partner—one that anticipates needs, surveys the task at hand, chooses the best approach, and executes with diligence. That’s precisely what defines an agentic AI. It doesn’t just echo commands; it formulates a clear plan, makes informed decisions, employs appropriate resources, and even double-checks its own work.

Think of it as assigning a task to a student who carefully divides it into manageable steps, conducts thorough research, and meticulously refines their work, instead of hastily presenting the first thought that comes to mind. Such behaviors aren’t random; they come from clear, repeatable design patterns. These “agentic patterns” guide AIs toward reliable thinking, independent actions, and self-verification, ensuring mistakes are caught well before they reach your desk.

In practical terms, these structured patterns transform a simple chatbot into a disciplined, methodical problem-solver. They prevent the AI from wandering off-topic or missing critical steps, significantly improving the quality of its output compared to a one-shot approach. For instance, self-checking agents actively reflect on their results, which helps them deliver more precise and effective code. Simply put, these patterns provide the AI with a clear framework, akin to giving it guidelines and guardrails, ensuring smooth performance even on complex tasks.

What are agents, really?

Let’s kick things off by clearly defining what we mean by “agent,” because it’s a fresh term that’s used differently depending on who you ask. Some people call any specialized function or task an agent, but for our purposes, we’re looking at something broader. In that sense, an agent is a complete, end-to-end system that is assigned a specific role or goal.

With this foundation, it’s essential to recognize that these agentic systems aren’t binary, but rather exist along a spectrum. At one end, we have fully autonomous systems. These are AIs that can independently plan, decide which tools to use, and adjust their approach without strict instructions. At the opposite end are purely scripted workflows, where every step is pre-defined, and the AI simply fills in the blanks.

We’ll refer to the entire range as agentic systems, and you can control how much autonomy you want to give your agent:

• Workflow-based systems (0% agentic): Every step is explicitly scripted, and if required, the AI merely follows a precise checklist.

• Agentic workflows (1–99% agentic): Engineers define tools, boundaries, and conditions, but the AI decides the order, repetition, and when to seek assistance.

• Fully autonomous agents (100% agentic): The AI navigates tasks independently, guided only by essential safeguards like budget limits or iteration constraints.

In this course, we’ll explore this full spectrum, paying special attention to reusable patterns that help you design robust, capable agents.

What should you know before starting?

Before building these agentic systems, let’s ensure you’re equipped with the right foundations. You don’t need to be an AI expert or hold a PhD in machine learning, but here’s what you’ll need:

• Python programming: Read and write Python code to configure and enhance your agents.

• Basic understanding of LLMs: Familiarity with how large language models like GPT-4.1 or Claude operate, prompting strategies, token limits, hallucinations, and related concepts.

• Comfort with APIs: If you’ve used Python or tools like Postman to interact with APIs, you’re all set. We’ll integrate tools extensively through APIs.

We’ll primarily use the OpenAI Python SDK, so come prepared to consider entire systems rather than individual prompts.

What will you gain from this course?

By completing this course, you’ll confidently design and build intelligent agentic systems capable of:

• Independently planning, and executing complex tasks.

• Iterating, self-reviewing, and refining their outputs until they achieve their goals.

• Accessing external tools to gather data and perform actions.

• Collaborating effectively, and distributing tasks among specialized agents.

• Working on multiple aspects simultaneously, and delivering faster results.

You’ll move beyond simple prompting, stepping into the role of an agent architect. You’ll learn the art of designing intelligent workflows that don’t require constant supervision, allowing you to prototype and deploy sophisticated agent systems using the OpenAI SDK.

Think of this course as equipping you with the AI systems designer skill, an upgrade that strengthens every future system you build, making them clearer, smarter, and more effective.

Real talk: Agents aren’t just another passing trend. They represent the next evolution in AI development—systems capable of reasoning, planning, adapting, and taking meaningful action. If you want to create solutions that truly think rather than simply respond, perfecting these agentic patterns is essential. It’s the difference between crafting superficial prototypes and building powerful tools.

Let’s get started.