Building AI Agents: A Practical Certification Course

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About Course

This certification course is designed to help developers and AI enthusiasts truly understand how AI agents work, how they think, act, and solve problems in real-world contexts. Through a mix of concepts, hands-on labs, and real code, you’ll go from understanding the fundamentals of agents to actually building and deploying them, using the aiXplain platform and SDK as your toolkit.

Key Learning Objectives
  • Intro to AI agents: Understand what AI agents are, how they operate, and where they fit in the broader AI landscape.
  • Single vs. multi-agent systems: Explore how individual agents work independently vs. how they collaborate as a team to tackle complex tasks.
  • RAG and Agentic RAG: Learn how retrieval-augmented generation (RAG) works and how it can be extended and improved using agentic design patterns.
  • Tool integration with SDK: Use the aiXplain SDK to connect agents with tools, APIs, and external logic—turning simple ideas into powerful workflows.
  • Hands-on practice: Work through real Colab notebooks that walk you through building and deploying AI agents from scratch.
  • Additional topics: More upcoming…
Why Enroll
  • Get certified: Become an officially recognized aiXpert, ready to take on client work or contribute to AI solution development.
  • Practical skills: From building agents to integrating APIs to managing tool-based workflows, this course gives you skills you can apply immediately.
  • Innovation: Gain a deep understanding of agentic AI and how it’s shaping the next wave of intelligent systems.

This course is perfect for developers, IT professionals, AI enthusiasts, and anyone interested in harnessing the power of AI through the aiXplain platform, especially those interested in joining our Partnership Enablement Program. Upon completion, participants will not only understand the theory behind AI solutions but will also be adept at applying these concepts in real-world scenarios.

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Course Content

Getting Started with aiXplain
In this module, you’ll learn how to set up your aiXplain account and get familiar with the platform’s key features. From claiming your free credits and exploring your personal workspace to discovering over 38,000 AI assets and creating API keys—you’ll cover all the essentials to start building and deploying agents. You’ll also learn how to: Navigate the asset marketplace and evaluate model details Integrate assets into your own applications or agents Create and manage API keys for different projects Set up team workspaces for collaboration Track usage and manage credits through your wallet By the end of this module, you’ll be fully equipped to explore, prototype, and launch with aiXplain’s powerful AI tools and infrastructure.

  • How to get started with building agents using aiXplain SDK
    02:24

Module 1: Basics of AI Agents
We start with the basics—what AI agents are, why they matter, and how they differ from traditional AI workflows. You’ll learn how modern agents can reason, act, and adapt, and we’ll look at the evolution from simple LLM prompts to more autonomous, goal-oriented systems. After learning about the basics of AI agents, next we jump into understanding the deep ends of multi agents and advanced workflows.

Module 2: Build Your First AI Agent
Time to get your hands dirty. This module walks you through creating your very first AI agent using the aiXplain platform. You’ll learn the structure of an agent, how to define its task, and how to enhance its functionality by adding tools. Access the Google Colab here: https://colab.research.google.com/drive/1CKpjEoQvqO8hpLePOpcJeCJlH4bC8eec?usp=sharing

Module 3: Tools Integration with AI Agents
AI agents become truly powerful when they can interact with external systems. This module (Part 1,2,3) shows you how to connect agents to tools like Slack, JIRA, and Vercel. You’ll work through real examples such as: A Slack + JIRA sense agent for workplace productivity A Fitness Chef Agent enhanced with Vercel and Copilot tools for health and meal planning use cases.

Module 4: RAG and Agentic RAG
This is where things get deeper. You’ll explore how to build Retrieval-Augmented Generation (RAG) pipelines to fetch and ground external knowledge in your agent’s responses. Then, we’ll expand on this by introducing Agentic RAG, where multiple agents collaborate or take on subtasks within a RAG flow. You’ll also build: A complete RAG pipeline A fully functional Coding Tutor Agent

Module 5: Agent Builder
Not everyone wants to write code. This final module introduces Agent Builder for macOS, a no-code low-code interface to build, customize, and deploy AI agents visually. It’s perfect for non-developers or those looking to speed up prototyping.

Module 6: LLM Benchmarking For Agents
This video is a practical guide by Shreyas Sharma from aiXplain on benchmarking LLMs for AI agents. He explains why systematic evaluation is crucial for selecting the right LLM, covering the three key components: datasets, models, and metrics. Using a medical nutrition chatbot example, he demonstrates the complete process from dataset selection on Hugging Face through running benchmarks on the aiXplain platform, comparing models across performance, cost, and speed metrics. The key takeaway is that proper evaluation helps make informed decisions about LLM selection rather than relying on guesswork.

Quiz

Agentic RAG Project
Complete the project to get certified Please note that your project will only be considered if you have signed a partnership agreement with aiXplain and paid the certification fee. The course is free, but certification is not. For more information, contact care@aixplain.com.

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