Part III: Existing Agent Orchestration methods dissimilar to agents-as-tools

Part II: Existing Agent Orchestration methods similar to agents-as-tools

Part I: Introduction

AI agents are autonomous programs designed to perform specific tasks by processing information and taking actions. Like with any LLM application, careful prompting is crucial for agents to get sensible and reliable results. One of the most robust and universal prompting techniques is ReAct, which combines step-by-step reasoning and acting.

As any experienced developer knows, for any reasonably mature system the time spent writing new code is tiny compared to the time spent debugging it, writing tests for it, and making sure these tests pass - and especially in a duck-typed language like Python, debugging often means literally stepping through the code with a debugger and inspecting the local state.

If you’ve ever tried to put Large Language Model (LLM)-based functionality, for example using AI agents, into production, you will have noticed that while it’s extremely easy to build something that sort of works, it can seem hard to impossible to build something that works reliably enough, let alone provides any guarantees of the output’s quality.