AI Agents in Practice: Build End-to-End AI Agents

The era of isolated AI prompts is giving way to something far more powerful: autonomous AI agents that can plan, reason, use tools, and complete multi-step tasks with minimal human intervention. This intensive hands-on course takes participants from the conceptual foundations of agentic AI all the way to deploying production-ready agents — no prior machine-learning background required, though familiarity with Python is helpful.

The first part of the course establishes the theoretical architecture of AI agents. We contrast classical chatbot interactions with agentic loops, and then dive deep into two leading frameworks: ReAct (Reasoning and Acting), in which the model interleaves thought steps with tool calls and observations; and ReWOO (Reasoning WithOut Observation), which separates the planning phase from execution for greater efficiency. Participants will build intuition for when each approach is appropriate and how to combine them.

In the practical workshops, participants design and build agents from scratch. They create custom tools — Python functions, API wrappers, database connectors — and integrate them into their agents. They add knowledge sources using retrieval-augmented generation (RAG), enabling agents to draw on private documents and real-time data. They craft system prompts that reliably guide agent reasoning, planning, and tool selection, and they learn the failure modes to watch for.

The final module covers observability and production readiness. Using an industry-standard observability platform, participants instrument their agents to capture traces, token usage, latency, and error events. They learn to debug misbehaving agents, evaluate output quality systematically, and build the monitoring infrastructure that responsible production deployments require. Participants leave with a working agent, a tested deployment, and the confidence to build the next one independently.