Linear harness
AI agents complete tasks in a straight line, one after another.
A linear harness is a way to organize AI agents. Think of it like a production line. Each step in the process is handled by a different AI agent, or a different part of the same AI. The output of one step becomes the input for the next, moving the task forward in a specific sequence.
For example, imagine an AI agent designed to write a blog post. A linear harness might have one agent find relevant information, a second agent draft the post based on that information, and a third agent edit and proofread the draft. The agents work one after another to complete the final blog post.
A linear harness defines a sequential execution flow for AI agents, where tasks are processed in a fixed order. Each stage in the harness performs a specific function, and its output serves as the input for the subsequent stage. This contrasts with more complex orchestration methods like agent trees or parallel processing, offering simplicity and predictability.
This approach is well-suited for well-defined, multi-step processes where the sequence of operations is critical and dependencies are linear. Tradeoffs include potential bottlenecks if one stage is significantly slower than others and a lack of flexibility for tasks requiring branching logic or dynamic adaptation.
This term is appearing in recent news as companies like Google explore ways to build more autonomous AI agents. These agents need structured ways to process complex tasks, and linear harnesses offer a straightforward method for sequential execution. This approach helps in breaking down difficult problems into manageable steps for AI.
- How to use AI Agents for automated code reviewsGoogle · Google Cloud Tech on YouTube ·
- Harness engineering and context curationGoogle · Google Cloud Tech on YouTube ·
- Agent Harnesses Explained: Inside the Stack Behind Antigravity, Claude Code & CursorGoogle · Google Cloud Tech on YouTube ·
- Google Gemini
- Google Cloud Tech specifically mentions and explains agent harnesses, including linear ones, as part of the technology stack enabling autonomous AI coding with Gemini.
- Agent orchestration
- While linear harnesses are a form of agent orchestration, o
- Prompt chaining
- Prompt chaining focuses on linking multiple prompts together, o
The term "harness" in this context likely originates from software engineering, referring to a framework or system that controls and directs operations. Its application to AI agents describes a structured way to "harness" their capabilities in a sequence.
Try using a simple chatbot to perform a multi-step task. For example, ask it to "Find the capital of France, then tell me its population, and finally, what is the main river that flows through it?" Observe how it attempts to complete each step sequentially.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- PromptRole-play a student struggling with math for AI tutor simulationSimulate a student's math struggles and uncertainty for AI tutor evaluation. · 5 min
- TemplateReusable workflow templates for healthcare teamsTeams can create and share templates for common workflows like documentation or prior authorizations. · 15 min
- TemplateTemplate for building an AI tool tailored to your workA structured approach to defining and building a custom AI tool for specific business needs. · 15 min
- TemplateEvals CSV prompt list for agent skillsA CSV file template for listing prompts used in evaluating agent skills. · 20 min
- HeuristicDouble coding agent task completion with docs linksProviding coding agents with a documentation link significantly increases their task completion rate. · 2 min
Questions people ask
- What is an AI agent?
- An AI agent is a program that can perceive its environment, make decisions, and take actions to achieve specific goals.
- How is a linear harness different from other agent setups?
- A linear harness follows a strict, one-after-the-other sequence of tasks, unlike more complex setups that might involve branching or parallel actions.
First seen on Subvolts Sep 14, 2026. Updated Oct 7, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.