Agentic AI Course — Build Production AI Agents, Live

Prompt engineering is about getting one good response from a model. Agentic AI is about building a system that takes a goal, plans steps, calls tools and APIs, uses retrieved context, handles failures and reports back — with logging, evaluation and cost control around it. This course is about that system, and treats the model as one component.

Working Python and comfort with REST APIs and JSON. You do not need machine-learning theory, a maths background or GPU experience. If Python is the gap, take the Python Programming course in this track first — the two are designed to run back to back.

You work with current agent frameworks and the tool-calling APIs from the major model providers, plus vector databases for retrieval and a tracing/eval layer. Frameworks change quickly, so the course deliberately teaches the underlying patterns — tool schemas, context management, orchestration, evaluation — so your skills survive the next release.

Yes. You build multiple agents end to end: a retrieval agent over a real document set, a tool-using agent that calls external APIs, and a multi-agent workflow with a supervisor — each deployed behind an API with tracing and an evaluation harness, so they hold up as portfolio work.

Product companies, GCCs and consultancies building internal copilots and automation, across India, the US, the UK, Germany, Singapore and the UAE. Job titles vary — AI Engineer, Applied AI Engineer, LLM Engineer, AI Platform Engineer — but the skill they screen for is exactly the agent-building and evaluation work in this track.