AI Agents & Workflows - The Practical Guide
Learn how to programmatically build AI Workflows & Agents. All the theory, plenty of examples.
Enroll in Course
"AI Agents" is a buzzword.
That's a pity, because AI powered workflows and agentic systems can unlock an entire new world of opportunities.
From data transformation to content generation, customer service or automated research. Your imagination is the limit!
And this course will get you there: You'll be able to build your own AI-powered applications and agentic systems!
This course will teach you what exactly "AI Agents" are, how they differ from (and still relate to) "AI Workflows" and how you can build and use both.
With this course, I decided to take a step back from the buzzwords and instead give you all the crucial theory, plenty of examples and concrete code snippets to ensure that you fully understand how LLMs, "normal code" and data work together to bring AI-powered applications to life.
With this course, you'll get:
- A thorough explanation of "AI Workflows" & "AI Agents"
- Lots of examples (incl. content generation, customer support, automated research)
- Detailed explanations that allow you to apply the knowledge no matter which programming language you're using
You'll learn:
- How to build AI Workflows & Agents from scratch
- How to use OpenAI's models (via API & SDK) programmatically
- How to transform input data with AI
- How to build AI-powered automations
- How to interact with third-party services like Slack
- How use AI for self-evaluation
- How to manage short- and long-term memory for your AI Agents
- How to build Multi-Agent systems and share data between agents
- How to split work between universal and specialized Agents
- How to add a Human-in-the-Loop step to your workflows
- And so much more!
All code examples in this course use Python and the OpenAI API / SDK but you'll be able to apply the knowledge to any programming language or model you're using in your day-to-day work.
4
hours
50+
lessons
Course Curriculum
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StartModule Introduction (2:21)
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StartNo Code vs With Code (2:07)
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StartBuilding AI Apps & Using AI Programmatically (4:12)
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StartProprietary vs Open (Local) LLMs (5:51)
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StartUsing Open LLMs
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StartUnderstanding Our Development Environment (2:29)
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StartCreating a New Python Project (using "uv") (1:43)
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StartOpenAI Setup & Pricing (5:41)
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StartGetting Started With A First Example Workflow (2:44)
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StartPreparing HTTP Requests For The OpenAI API (8:40)
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StartChoosing & Using a Model (2:04)
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StartPrompt Engineering (4:35)
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StartExtracting & Using the LLM Response (4:50)
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StartMore on the OpenAI API & SDK
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StartCode Deep Dives vs Provided Code Snippets
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StartUse Those Docs! (1:38)
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StartUsing The OpenAI Python SDK (5:49)
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StartLeveraging Few-Shot Prompting (4:02)
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StartGenerating Prompts Dynamically With Dynamic Content (2:12)
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StartBuilding Multi-Step & Multi-Model Workflows (6:47)
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StartWorkflows vs Agentic Systems (1:34)
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StartUsing Locally Running Open Models via Ollama (8:08)
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StartEnforcing & Using Structured Outputs (10:53)
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StartMore On JSON Schemas & Structured Outputs
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StartStructured Outputs via SDK & Pydantic (3:56)
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StartUsing Prompt Engineering To Control Output
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StartOnwards To Another Example (5:38)
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StartGenerating Images In a Workflow (6:27)
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StartControlling Workflow Execution with Control Flow Adjustments (2:45)
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StartControl Flow In Action (8:42)
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StartAdding a "Human In The Loop" (6:51)
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StartIntegrating External Services - Example: Slack (6:21)
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StartImportant: Potential Problems & Security Risks
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StartModule Introduction (1:36)
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StartHow LLMs (Do Not) Use Tools (6:04)
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StartImplementing Tool Use From Scratch (11:24)
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StartUsing OpenAI's Function Calling Feature (10:23)
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StartBuilding a Multi-Tool Versatile Agent (11:16)
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StartUsing Advanced AI Models
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StartBuilding Reusable Elements With Classes (7:53)
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StartGetting Started with a Multi-Agent System (7:14)
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StartBuilding & Connecting Specialized Agents (10:24)
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StartExtracting Website Content
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StartUniversal vs Specialized Agents (3:38)
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StartAgent Memory: Short-Term & Long-Term (5:21)
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StartWrap Up (1:09)
Course Prerequisites
Basic understanding of programming concepts is required
No prior AI experience necessary to succeed in this course
All pre-requisites are covered by courses in our "Academind Pro" Membership.
Don't take my word for it - here's what other students are saying.
Ira Scott Herman
The more I go through this course, the more incredible I think it is! The course is well organized and practical knowledge. The instructor uses best practices, and is consistently updating the course so I just keep getting more and more value from my initial purchase.
J. Scott Cromie
I feel like I learned a ton and feel like I can build custom apps to store on the flat thingy I keep in my pocket! Thanks for the course. It's great. Highly recommended.
Rachael Bouissey
As always, great teacher. I got a lot out of the React course and React Native is starting out just as helpful. Never would have made it through bootcamp without Maximillian!
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We're so convinced by our course quality that we guarantee your success and provide a full refund within the first 30 days if you're not happy with the course.
Your Instructor
As a self-taught professional I really know the hard parts and the difficult topics when learning new or improving on already-known languages. This background and experience enable me to focus on the most relevant key concepts and topics. My track record of many 5-star rated courses, more than 2,500,000 students worldwide as well as a successful YouTube channel with 900.000 subscribers is the best proof for that.The most rewarding experience for me is to see how people find new, better jobs, build awesome web applications, work on amazing projects or simply enjoy their hobby with the help of my content.That's why, together with Manuel Lorenz, I founded Academind to offer the best possible learning experience and to share the pleasure of learning with our students.
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