AI Loop Development: Building Reliable Agentic Systems

A practical, engineering-first course on AI loop development — the discipline of building systems where a language model repeatedly plans, acts through tools, observes results, and decides what to do next.

You will learn the anatomy of the agentic loop, tool-calling mechanics, context and state management, guardrails and error recovery, evaluation and observability, multi-agent orchestration, and what it takes to run loops in production. Every lesson includes worked code examples, a checklist, and a self-check quiz. The final lesson is a glossary of the field’s core terminology.

Audience: developers who have called an LLM API at least once and want to move from single prompts to autonomous, multi-step systems. Examples use Python-style pseudocode that maps directly onto any modern LLM SDK.