Five AI experts collaborating on agentic AI research

Meet Your Guides

Five AI experts. Five voices. One goal: make agentic AI click.

Every conversation on Guided Agentic AI is led by one of five AI teaching personas — each with a distinct specialization, teaching style, and personality. Choose the guide who fits the way you think.

Not sure who to pick? Start with Dr. Aria Chen for foundations or Prof. Marcus Webb for practice problems. You can switch guides at any time from the chat panel.

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Dr. Aria Chen at a whiteboard covered in transformer architecture diagrams

Dr. Aria Chen

Professor of Machine Learning & Language Models

LLMs, prompt engineering, and model internals

LLMs, prompt engineering, and the science of getting models to do what you mean.

Builds up from first principles, uses precise technical language, and always connects abstract concepts to concrete examples. Comfortable with both conceptual and code-level explanations.

Large language modelsPrompt engineeringModel internalsTokenizationFine-tuning
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Prof. Liam Carter in a server room reviewing a multi-agent system architecture diagram

Prof. Liam Carter

Professor of AI Systems Engineering

Agent architectures, systems thinking, and production AI

Agent architectures, systems thinking, and the engineering discipline of building AI that works.

Starts with the big picture architecture, then drills into components. Uses diagrams, analogies, and real-world engineering examples. Always asks 'what breaks?' and 'what scales?'

Agent architecturesSystem designMulti-agent systemsProduction engineeringTrade-off analysis
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Dr. Sofia Reyes at a conference speaking about AI governance and ethics

Dr. Sofia Reyes

Professor of AI Ethics & Safety

AI safety, ethics, alignment, and governance

AI safety, alignment, and asking the questions that matter before it's too late.

Frames technical problems within their broader ethical and social context. Uses thought experiments, case studies, and historical analogies. Asks hard questions and sits with uncertainty.

AI safetyAlignmentEthicsGovernanceSocietal impact
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Jordan Blake at a standing desk reviewing code for a production AI agent deployment

Jordan Blake

Senior AI Engineer & Developer Advocate

Practical implementation, production systems, and developer experience

Practical AI engineering — what actually works, at scale, in the real world.

Shows working code before explaining theory. Uses real-world examples from production deployments. Cuts through abstraction to what actually matters when you are building something real.

Practical implementationProduction AITool useCode examplesDebugging
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Dr. Omar Hassan at a conference presenting on emergent behavior in multi-agent AI systems

Dr. Omar Hassan

Professor of Multi-Agent Systems & Complex AI

Multi-agent systems, emergent behavior, and complex adaptive systems

Multi-agent systems, emergent behavior, and the surprising complexity that arises when AI agents interact.

Explores ideas from multiple angles, draws connections across fields, and is comfortable with open questions. Uses simulations, analogies from biology and economics, and cross-disciplinary thinking.

Multi-agent systemsEmergent behaviorComplex systemsAI researchTheoretical foundations
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Not sure where to start?

Begin with Introduction to Agentic AI and Dr. Aria Chen. You can switch guides, topics, and difficulty at any point in your learning journey.