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. Introduction
There is no doubt that big language models are really powerful but they cannot go beyond their training data or communicate directly with the world. In the same place, AI agents have changed the game. They not only produce the text but can also act, reasoning and complement the multilateral tasks, which they feel close to a real assistant that can work for you. You may have seen a lot of resources, but for this article we will be taking a big picture tour. I will share 5 early -friendly projects: Some of the use of Azigar + from the beginning are some of the famous AI agent framework. After widespread research, I have designed and selected these projects in such a way that each project teaches a different angle of what agents can really do. So, let’s start.
. 1. Making AI calendar agent in pure agent
Link: https://www.youtube.com/watch?v=bzypscbti8
This tutorial runs through the construction of a calendar/scheduling agent using purezer without heavy framework or cloud dependence. You will find a hand -on demo of the Agent Loop: Analyzing intentions, planning steps, calling calendar APIS, and verifying or handling conflicts. It has verified and performance of CRUD operations with Google Calendar or similar services, as well as practical points to analyze natural language hours and avoid double booking. The instructor gives you step -by -step guidance, showing how to handle requests such as “Schedule Meeting on 3PM” or “What is in my Calendar” and to map them on tool calls, such as bringing events or creating new work. Once your agent can manage your schedule reliably, it already feels that you are talking to a personal assistant who is capable of acting, not just talking.
. 2. How to make a coding agent from the beginning
Link: https://www.youtube.com/watch?v=lxgfhpq1gsi
Zain Hassan’s workshop -style directive AI’s developer relationship team operates through the construction of a coding agent from the beginning, relying on without any pre -built framework. You will start with a simple chat loop, then add tools such as file readers, shells, and search capabilities, followed by secure sandboxing rules and repetitive diagnosis and debugging. On the way, you will look for parallel, serial, conditional and looping agent workflows, learn how to use LLM as routers and reviewers in the agent pipeline, and review practical code examples to enforce these workflows. Once your agent can automatically prepare, test, and improve pieces, it seems that your own personal couple’s programmer is ready to cooperate.
. 3. Material creator agent from the beginning
Link: https://www.youtube.com/watch?v=pm9zr7wgjx4
Through the staff CEO, Jeyo Mora, this step is shown in the walkthrough how to develop a material creator from the beginning using staff, zipper, and cursor, which is ideal for creators and business people who want agent -powered automation. You will learn how to set the workflows from the end to finally handle content theory, auto drafting, publishing and distribution of cross posts. The tutorial covers both nun code and code -based approaches, showing how wire triggers, functions, rate limits and QA measures are shown so that you maintain quality control, such as social posts, news letters, or short -form video scripts. On the way, Joo guides you by improving integration tools, debugging, and agent performance, with practical examples, including multi -agent flow construction, customized PDF reports, and developing materials projects.
. 4. Research agent with Pedentic AI
Link: https://www.youtube.com/watch?v=762Sqd7iw6y
Angelina’s hand -on guide, co -founder of AIVP and data and Transform AI Studio, and co -founder of computer science and co -founder of Transform AI Studio, Mahdi runs you from the beginning to make you a structural research agent using Padintic AI and Vanilla Azigar. You will learn how to describe the output tipped typed schemes and write down small agents who find the web, download pages or PDFs, summarize the results, and add the overall results to clean, structural notes or emails. Tutorial shows how to combine web search APIs, document downloaders, and LLM summary, while taking advantage of podantic models ensure that the output is predicted, reliable and able to read. This approach makes it ideal for creating reproductive research assistants or literature survey boats.
. 5. Advanced AI agent with search
Link: https://www.youtube.com/watch?v=cUC-Hyjpnxk
This deep tutorial production style research agent of Tim from Dave Launch is designed for learners. You will learn how to make a multi -phase, graph -based workflower, which directly web scraping and searching, related filtering, deduction, and reputation. The guide covers advanced architecture patterns such as questioning routing, crawler design, and enormous indexing, as well as practical concerns for politeness, proxies and rates. By adding a Lang Graph with real -time searching from sources like Google, Bang, and Reddate, you will create an agent that not only causes but actively searches and collects the latest information. This project is ideal for everyone to go beyond toys agents and build an extended, real -world research assistant.
. Wrap
These five projects are far ahead of “just making model chat”. My gesture: Do not be caught in completing the same idea. Choose one of the one that makes you excited, prepare it, and then experience it. The more agent samples you discover, the easier you can combine, match and invent yourself.
Kanwal seals A machine is a learning engineer and is a technical author that has a deep passion for data science and has AI intersection with medicine. He authored EBook with “Maximum Production Capacity with Chat GPT”. As a Google Generation Scholar 2022 for the APAC, the Champions Diversity and the Educational Virtue. He is also recognized as a tech scholar, Mitacs Global Research Scholar, and Harvard Vacod Scholar as a Taradata diversity. Kanwal is a passionate lawyer for change, who has laid the foundation of a Fame Code to empower women in stem fields.