Edison AI Academy · Builders
AI Agentic Specialist
Year 1 taught you to build. Year 2 teaches you to engineer.
For ambitious students learning to connect ideas across disciplines and use AI to research, build, analyse, and communicate faster.
- Age range
- 14–19
- Duration
- 38 weeks · 4 terms
- Format
- Hybrid
- Level
- Intermediate
- Cohort
- 10–14 students
- Outcome
- Multimodal portfolio project
What is the AI Agentic Specialist program?
The AI Agentic Specialist is Edison AI Academy's second-year program, for students aged 14–19 who have completed the AI Hypergeneralist or equivalent. Across 38 weeks it turns capable builders into disciplined engineers — teaching Git, automated testing, databases, FastAPI, production retrieval pipelines, multi-agent orchestration, deployment, monitoring, security, and AI governance.
Students work in engineering teams, deploy live applications, and graduate with five major builds, technical writing, security and bias audits, and a GitHub profile that reads as professional work. It prepares students for the AI Associate Architect program and for technical study beyond school.
Cross-disciplinary fluency for ambitious learners.
For ambitious students learning to connect ideas across disciplines and use AI to research, build, analyse, and communicate faster.
The AI Agentic Specialist is the shift from making things that work to making things that work reliably, at scale, for real users, with accountability.
Students take on the full stack of modern AI engineering — Git, automated testing, databases, FastAPI, production retrieval pipelines, multi-agent orchestration, deployment, monitoring, security, and governance — and they work in engineering teams, on real sprints.
Portfolio projects stop looking like schoolwork. Students deploy live applications, run security audits, write technical posts, and present to audiences that include working professionals. By graduation, a student's GitHub profile reads as an engineer's.
- What this program is
Cross-disciplinary fluency for ambitious learners.
- Who it is for
Students with wide interests who want range, adaptability, and creative intelligence.
- What students build
Portfolio-worthy multimodal AI project
- What students learn
Medium-length cycles with growing autonomy. Students lead their own build sprints, defend their decisions in studio critique, and ship a portfolio-worthy multimodal artefact.
- How students are taught
Small cohorts of 10–14 students, mentor ratio 1 mentor : 6 students. Edison runs on case-based learning, rotating studio roles, prototype-first modules, and year 2 exhibition.
- Final outcome
Multimodal portfolio project
The Edison Learning Engine
How a program assembles into capability.
Edison programs are not a sequence of lessons. They are a learning engine, six parts that fit together to produce thinkers who can build with AI, defend their work, and lead.
Assembly
- 01
Curiosity Core
Students begin with questions, not templates. They learn to investigate problems before reaching for tools, the genuine intellectual engine of the program.
- 02
AI Fluency Layer
Students learn how to use AI systems as thinking partners, research assistants, and creative collaborators, across prompts, models, tools, and workflows.
- 03
Builder Studio
Students move from ideas into prototypes, turning abstract concepts into tangible AI-powered outputs through structured, hands-on experimentation.
- 04
Systems Thinking Ring
Students learn to see the whole system: users, constraints, incentives, workflows, ethics, and impact, the connective tissue that turns parts into outcomes.
- 05
Communication Lens
Students learn to present their thinking clearly, explain how their solution works, defend their decisions, and refine in response to critique.
- 06
Final Prototype Engine
Every module ends with a working prototype that students can demonstrate, explain, and improve, the assembled output of the entire learning engine.
The learning pathway, term by term.
Thirty-eight weeks across four terms, roughly 135 hours. The work shifts from building features to engineering systems that hold up under real use.
- T01
Weeks 1–10
Term 1 — Engineering Foundations
The engineering mindset, Git and collaboration, object-oriented Python, automated testing with pytest, SQL databases, and FastAPI — applied to a first production-grade tool.
- Git
- Object-oriented Python
- Testing
- Databases
- FastAPI
Artefact A production-grade AI tool
- T02
Weeks 11–20
Term 2 — AI Architecture
Production retrieval pipelines, advanced retrieval, ReAct agents built from scratch, multi-agent orchestration with MCP, and evaluation pipelines that measure whether any of it works.
- Production RAG
- ReAct agents
- Multi-agent systems
- Evaluation
Artefact A multi-agent system and a RAG application
- T03
Weeks 21–30
Term 3 — Production + Leadership
Deployment to live URLs, monitoring and observability, security audits, and AI governance — the EU AI Act and ISO 42001 — alongside a team project run as real Agile sprints.
- Deployment
- Monitoring
- Security audit
- AI governance
- Teamwork
Artefact A deployed, monitored team application
- T04
Weeks 31–38
Term 4 — Capstone + Exhibition
An individual capstone: architected, built, evaluated, security-audited, deployed, and presented at the Year 2 Exhibition to an audience that includes industry professionals.
- System architecture
- Capstone delivery
- Technical presentation
Artefact Year 2 capstone, deployed and defended
Students don't just learn AI. They produce evidence.
Every project is built to a professional standard — version-controlled, tested, documented, deployed. The portfolio is the argument, and it is made of working systems.
- 01
Workflow blueprint
- 02
Multimodal prototype
- 03
Public defence
Shipped, reviewed, and documented — the portfolio of an engineer, not a student.
Students learn with the tools modern builders actually use.
The AI Agentic Specialist toolkit is curated to match the depth and ambition of this program. Students learn to choose, combine, and switch between tools, not memorise a single platform.
Category
Reasoning & Research
- ChatGPT
- Claude
- Perplexity
- NotebookLM
Category
Design & Content
- Figma
- Canva
- Midjourney
- Runway
Category
Automation
- Make
- Zapier
- Airtable
- Notion
Tools evolve. Edison teaches the durable thinking, choosing the right tool, combining tools well, and switching when a better tool emerges.
Edison AI Academy
Founding faculty
Students learn from instructors who combine AI fluency, curriculum design, strategy, and practical implementation experience. Edison AI Academy is built around the belief that young people do not just need to learn tools, they need to learn how to think, build, and lead with them.
Cohorts are deliberately small (10–14 students, mentor ratio 1 mentor : 6 students), so every student is known, stretched, and held to a high standard.
We teach young people how to think, build, and lead with AI, not just how to use it.
Edison AI Academy is a selective program. Fees reflect the small cohort size, mentor ratio, and the depth of the work students complete.
Flexible payment plans may be available for accepted students. Bursaries and scholarships are reviewed individually as part of the admissions conversation.
We frame this as an investment in your child's future readiness, not a transactional fee for content delivery.
AI Agentic Specialist — answers we get asked most.
Not sure if AI Agentic Specialist is the right fit?
Speak with Edison AI Academy about your child's goals, current skill level, and best-fit pathway. We will recommend the right program and the next available cohort.
Three short steps. We'll match you to the right Edison pathway.
Our admissions team reviews every application personally and replies within one business day. The form takes about two minutes.
Your AI Agentic Specialist preference travels with you to the application form — we'll know exactly what you're interested in.