In this episode of The Agent Factory , we explore the reality of building with autonomous agents alongside Ryan Lopopolo, a software engineer at Google Cloud and the person who coined the term agent harness . From throwing out manual code editors to treating team collaboration like leveling up RPG stats, Ryan breaks down how grounding models in rich context and shifting interventions left unlocks high levels of agent autonomy.

This post guides you through the key ideas from our conversation. Use it to quickly recap topics or dive deeper into specific segments with links and timestamps.

An AI agent as we're defining it here is a large language model (LLM) plus an agent harness .

Think of the harness as everything wrapped around the LLM that isn't the model itself. For example, if you're working in Google Antigravity using Gemini 3.8 Flash , Gemini Flash is the LLM and Google Antigravity is the harness.

While an unassisted model can answer simple questions out of the box, it can't check live conditions or interact with your workspace on its own. When a user asks a question like "Why is the sky blue?" , an unassisted LLM can respond without issue. However, when asked a question like "Should I wear a raincoat today?" , the model can't answer on its own because it lacks the necessary data. The harness catches the intent, queries live weather tools, bundles that context back into the prompt, and hands it to the model to produce an informed answer.

Tilde Thurium sat down with Ryan Lopopolo to discuss what it takes to run fully autonomous coding workflows in production. See the summary below!

The term agent harness grew out of Ryan's extensive work on autonomous coding agents, culminating in a February 2026 essay on leveraging coding models in an agent-first world. Ryan shared that he hasn't opened a traditional code editor since May of last year, maintaining that streak through his transition into Google Cloud . In this paradigm, engineers no longer author or review individual lines of syntax; instead, they operate at the level of natural language specifications and inspect the final artifacts, such as pull requests, documents, and spreadsheets. Then they determine whether the end result meets organizational standards.

Upfront harness investment pays off by allowing engineers to become lazy prompters. When the repository contains structured documentation, clear interfaces, and discoverable tools, you do not need to paste walls of text into a prompt box every morning