Building Agentic AI and Autonomous Agent with Python & Groq

Welcome to Building Agentic AI and Autonomous Agent with Python & Groq course. This is a comprehensive project based course where you will learn how to build cutting edge AI agents that are fully autonomous and able to make their own decisions without being directed by humans. This course is a perfect combination between Python and AI agents, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in system automation.

In the introduction session, you will learn the basic fundamentals of agentic AI, such as getting to know its use cases, its workflow, and the difference between regular AI and agentic AI. Then, in the next section, you will learn about the basic concepts of prompt engineering and context engineering, specifically you will learn how to design effective prompts, structure context, and guide your AI agents toward producing accurate results. Afterwards, we will start the project.

Firstly we are going to build AI research assistants and report generator agents using Groq and Llama. This will be a truly agentic system, it will use the ReAct framework, meaning it will think step by step, perform reasoning, take actions autonomously, and reflect before the next move. It will also be able to perform real time web searches using Exa, gather insights, and generate professional reports without being told exactly what steps to take.

Then, in the second project, we are going to build AI procurements & risk control agents using Openrouter and DeepSeek. This will include multi agent autonomous collaboration. A Procurement Manager Agent will create a procurement plan, and then a Risk Analyst Agent will autonomously review the plan.

These two agents will interact, debate, and refine the plan together without human intervention, ensuring both cost effectiveness and risk mitigation. Following that, in the third project, we are going to build AI business proposals negotiator agents using Mistral AI and Mailjet. This agent will consist of three fully autonomous agents, a legal agent, a benchmark agent, and a negotiation agent.

They will review proposals, benchmark against competitors, and negotiate terms. The Negotiation Agent will also send counter offer emails autonomously using Mailjet. In the fourth project, we are going to build an AI pricing optimization agent using Groq and Gemma.

This AI agent will help us to autonomously discover and optimize product prices. Firstly, it will perform a web search to gather market price ranges. Then, two autonomous agents will debate, one will argue for setting a higher price, while the other will argue for a lower price.

They will continue their discussion until they reach an optimal price agreement and all of these happen without human direction. Lastly, at the end of the course, for the fifth project, we are going to build an AI hiring and talent management agent using Gemini and Mailjet. This agent will act as a fully autonomous HR manager.

It will generate job descriptions, analyze submitted resumes, and rank candidates. In addition, the agent will also send invitation emails via Mailjet, draft technical interview questions, and even schedule and send meeting links. Firstly, before getting into the course, we need to ask this question to ourselves, why should we build autonomous AI agents?

Well, here is my answer, autonomous AI agents can help us to automate repetitive tasks, reducing the need for constant human involvement. They also help to enhance efficiency and productivity by performing complex workflows and ensuring tasks are completed on time.