Go from understanding what AI agents are to building and demonstrating a working prototype — no prior coding experience required.
10
Weeks
21+
Lessons
3
Technical Tracks
100%
Free Forever
Introduction
This course will teach you about agentic AI — autonomous systems that perceive information, plan actions, use tools, remember context, and verify their own results — using real tools and frameworks including Make, n8n, OpenClaw, Hermes Agent, and Agno.
The program is built around a single conviction: the most powerful thing a high school student can do in the current moment is learn to build AI agents that solve genuine problems in domains they care about. You do not need a university degree or years of programming experience. You need curiosity, a real problem worth solving, and the ability to think carefully about what you are building and why.
This course is completely free, without ads, and open to any student accepted into the program.
Understanding Agentic AI
While this course introduces the foundations of artificial intelligence and large language models, its core focus is agentic AI — a specific and rapidly growing area that has transformed what is possible with software.
What Is the Difference?
AI and Machine Learning
The broader fields focused on enabling computers to learn from data, recognise patterns, and make predictions. Traditional ML encompasses techniques like classification, regression, and recommendation.
Large Language Models (LLMs)
AI models trained on vast amounts of text that can understand and generate human language across a wide range of tasks. Models like Claude, GPT-4o, Llama, and Gemini are examples.
Agentic AI
Systems built on top of LLMs that can perceive inputs, plan multi-step actions, use external tools (APIs, databases, browsers), maintain memory across a session, and verify their own outputs before completing a task. An agent does not just answer a question — it takes action.
What to Expect
The program is divided into four phases across ten weeks:
Phase
Weeks
Focus
What You Cover
Phase 1
1–2
Foundation
What AI agents are, how LLMs work, real agent demos, AI ethics, the business of AI, and a guest speaker from a real agent builder.
Phase 2
3–4
Exploration
The Example Gallery — 12 worked agent examples. Technical track selection, first hands-on tool sessions, and Project Idea Brief submission.
Phase 3
5–8
Build
Architecture design, environment setup, building your prototype, documentation, ethics review, mentor mid-build feedback, iteration and testing.
Phase 4
9–10
Showcase
Final submission, launch pathways, and your program certificate.
Three Technical Tracks
All tracks cover the same conceptual content in Phases 1 and 2. Track-specific content begins in Week 4. Choose the track that matches your current skill level.
Track A
No-Code
Make · n8n
Students with no coding background
Track B
Low-Code
OpenClaw · Hermes · VSCode
Students comfortable with basic setup and configuration
Track C
Full-Code
Agno · Python · VSCode
Students confident with Python or eager to learn it
Who Is This Course For?
This course is built around the belief that domain knowledge — understanding healthcare, research, education, or any other real-world field — is at least as valuable as technical skill when it comes to building agents that matter.
✅ Good Fit If You:
✓Are curious about AI and want to understand it accurately — not just follow the hype
✓Have a problem that might benefit from automation or intelligent assistance
✓Can commit 4–6 hours per week across 10 weeks
⚠ Challenging If You:
⚠Want a passive learning experience — every lesson has a deliverable
⚠Want AI theory without building anything — this is project-first
⚠Are not yet comfortable using a computer for sustained work
What You Will Build
Every student produces four deliverables that constitute a genuine portfolio item.
01
Working Agent Prototype
Designed, built, and tested using your chosen track. Solves a real problem for a named group of users. Partial or scoped-down prototypes are accepted — what matters is that it runs.
02
Demo Recording
A 2–5 minute screen recording showing the agent working, with narration explaining what it does and honestly describing its limitations.
03
Project README
Documentation covering the problem, how the agent works, what data it uses, known limitations, and edge case test results. Minimum 400 words.
04
Ethics Review
Four dimensions — privacy, bias, transparency, accountability — each with a named concern and a specific design decision you made to address it.
🚀 Launch Pathways
Completing the program opens access to: incubator referral, IP briefing, open-source release on GitHub, publication support, and letters of recommendation. The best projects are selected for the Launch Pathway during the course closing session.
Frequently Asked Questions
Does completing this course lead to a certificate?+
Yes. Students who complete all four submission requirements receive a digital certificate from the bioERGOtech Foundation with a unique verification link that can be shared on LinkedIn or included in university applications.
Do I need to know how to code?+
No. Track A (Make and n8n) requires no coding at all. Track B requires basic comfort with installing software. Track C is for students who already know Python or are willing to learn during the program.
Can I work alone, or do I need a team?+
Solo projects are permitted with mentor approval. Teams benefit from shared domain knowledge and distributed skills. Most students find 2–3 people optimal.
What happens if I fall behind?+
The program has two formal mentor touchpoints — Week 7 and before final submission. Reach out early if you are struggling. The most common issue is over-ambitious scope — your mentor can help you cut to something achievable without losing the core value.
Are my project and code my own intellectual property?+
Yes. Everything you build is yours. The bioERGOtech Foundation does not claim ownership over student projects. The Launch Pathway includes an IP briefing session explaining your options.
Where can I ask questions?+
Each lesson page on the bioERGOtech portal has a questions section. For general program questions, use the cohort discussion channel. For project-specific questions, post in the track-specific channel so peers using the same tools can also contribute.
💡 After This Course
We recommend the Hugging Face LLM Course (free) for deeper technical grounding in transformers and fine-tuning, and the Agno documentation for Track C students who want to continue building.