Applied Agentic AI
Build AI agents with clear goals, prompt contracts, retrieval boundaries, tool permissions, evaluation checks, and production runbooks.
Start with the path, then open the rooms.
Work through the lessons in order. Each lesson starts with the concept, explains the vocabulary, then moves into examples, review checks, browser practice, and saved proof when it is useful.
Outcomes
Agent system design and evaluation
This course is organized around a role path, industry alignment, and proof a learner can keep.
- Separate model behavior from agent goals, tools, memory, and checks.
- Define tool permissions and stop conditions.
- Evaluate an agent through success criteria instead of trust alone.
- Agent specs with goal, tool, guardrail, success check, and escalation condition.
- Learner explanations of model responsibility versus system responsibility.
- Safe tool-boundary practice evidence.
Agent rooms should keep tool access explicit and require a verifiable success check before completion.
Proof Loop
Examples support the reading, but they are not completion evidence by themselves.
Check Work must pass inside the sandbox before protected rooms can complete.
Learners keep proof by writing expected result, actual result, and next improvement in their own words.
Lessons, checks, explanations, and completion
This map shows what the learner needs to do in each room: learn the concept, pass Check Work when required, explain the result, and keep completion proof.
Large Language Models Foundations
Check locked2-3 hours
Prompt Engineering Mastery
Check locked2-3 hours
RAG & Knowledge Integration
Check locked2-3 hours
Agent Architectures
Check locked2-3 hours
Tools & API Integration
Check locked2-3 hours
Production Agent Systems
Check locked3-4 hours
Useful resources for this course
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