Build Real AI Agents, From First Loop to Production
AI agents are programs that use large language models to reason, call tools, and take multi-step actions — handling tasks that static code cannot. This track covers everything from how an LLM agent loop actually works to deploying reliable, observable agents in production. The curriculum spans 60 courses across six levels (A1 to C2), grounded in the tools practitioners use today: LangChain, LangGraph, LlamaIndex, vector databases, and the function-calling APIs exposed by every major LLM provider.
What You Will Learn
You will start with the mechanics of LLM APIs and prompt engineering for agents, then work through the tool-use and function-calling pattern that makes agents genuinely useful. Core skills include building retrieval-augmented generation (RAG) pipelines and vector database integrations, constructing email, calendar, Slack, GitHub, and web-scraping agents, designing conversational memory and structured output parsing, writing agent tests and debugging agent loops, and applying the ReAct pattern in multi-tool agents. At the advanced end you will tackle multi-agent orchestration, long-horizon planning, computer-use and browser agents, code-generating agents, sandboxing and secure code execution, and agent governance with audit trails.
The Learning Path
The path opens at A1 with Intro to LLM Agents and moves through A2 foundations — prompt engineering, LLM APIs in practice, file system operations, and conversational memory. B1 broadens the toolkit with API integration, RAG basics, vector databases, observability, LangChain fundamentals, and domain-specific agents for documents, data analysis, and workflow automation. B2 deepens that knowledge through LangGraph for stateful agents, LlamaIndex, the ReAct pattern, memory architectures, async and event-driven design, agent safety and guardrails, and cost and latency optimisation. The C1 tier covers advanced RAG techniques, multi-agent orchestration, production deployment, fine-tuning models for agentic tasks, and multimodal pipelines. The track closes at C2 with Self-Improving Agent Systems and The Road to Autonomous Systems.
How It Works
Each course is broken into short, focused lessons you work through in the built-in code editor with real-time feedback. An AI tutor is available whenever you get stuck, and every exercise is grounded in runnable code so concepts stay concrete rather than theoretical.
