Agentic AI course
An AI agent is a language model that decides its next step, calls tools and checks the result. This course teaches you to build agents that are useful and safe, on top of solid LLM engineering.
The agent module (Module 10) builds on LLM application engineering (Module 6), retrieval (Module 8) and evaluation and security (Module 9). Its project is a finance-operations agent that reconciles invoices through MCP tools, with human approval.
An agent that works in a demo is not yet a product. Module 12 (Production AI & LLMOps) takes the Module 10 agent and puts it into production: a Docker container, CI/CD with eval gates, deployment, monitoring and alerts, cost engineering, and a written runbook for incidents and rollbacks. Read how to implement MCP in Python and building multi-agent systems to see the approach.
Every module unlocks in order, after the one before it.
From your first line of Python to production LLM apps, RAG, agents, evals and fine-tuning. Reverse-engineered from 2026 AI-engineer job descriptions.
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Agentic AI means systems where a language model plans and takes actions, such as searching, running code or calling an API, looks at the results and repeats until a goal is met, instead of answering once.
The Model Context Protocol is an open standard for connecting AI applications to tools and data sources, so one agent can use many services through the same interface. Module 10 teaches it hands-on.
Agents act, so mistakes cost more than a wrong answer. You learn to test agent behaviour, limit what tools can do and require human approval before risky steps.
Modules unlock in order. Agents build on LLM applications, retrieval and evaluation, so you reach them with the parts they depend on.
AI vs ML vs DL vs GenAI: deep notes, a hands-on lab and a quiz. No card. No catch.