Closed-loop harness
Also written: Closed-loop physical experiments
An AI agent's process that includes feedback and iteration for self-correction and improvement.
Imagine an AI agent working on a task, like writing a report. A closed-loop harness means the agent doesn't just finish the report and hand it over. Instead, it has built-in steps to review its own work, check for errors or areas that could be better, and then make corrections before it's considered done.
Think of it like a student proofreading their essay. The agent can go back, read what it wrote, identify problems, and revise it. This makes the final output much more reliable and accurate because the AI is actively improving its own work as it goes.
A closed-loop harness refers to an agent execution framework that incorporates mechanisms for feedback and iterative refinement. This allows an AI agent to evaluate its intermediate outputs, identify discrepancies or errors against a goal, and trigger corrective actions or replanning. The loop typically involves an observation phase, an action phase, and a feedback phase, enabling agents to achieve higher fidelity and robustness in complex tasks.
This contrasts with open-loop systems where an agent executes a plan without internal validation or correction. The primary tradeoff involves increased computational cost and latency due to the iterative nature, balanced by improved task success rates and output quality, particularly for tasks requiring precision or adaptation.
Frontier AI labs are increasingly using closed-loop harnesses to automate complex tasks. This approach allows AI agents to conduct physical experiments or code reviews, analyze results, and adjust their strategy without human intervention. The goal is to accelerate discovery and development by enabling AI to learn and iterate on its own.
- 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 ·
- AI for Atoms: How Periodic Labs is Revolutionizing Materials Engineering with Co-Founder Liam FedusOpenAI · No Priors ·
- Google Gemini
- Google Gemini is using agent harnesses, including closed-loop systems, to build autonomous AI coding agents that can solve complex problems without constant manual prompting.
- OpenAI ChatGPT
- OpenAI is involved in applications where AI orchestrates physical experiments, suggesting the use of closed-loop systems to manage the iterative process of materials engineering.
- Open-loop harness
- An AI agent execution process that completes a task without i
- Orchestration
- The management and coordination of multiple AI agents or tools to
The concept of closed-loop systems originates from control theory, where a system's output is fed back to influence its input for regulation. In AI, it's applied to agent architectures to enable self-improvement and error correction.
Use a coding assistant like GitHub Copilot or Google's Gemini in your IDE. Ask it to write a complex function, then review its output. If it has errors, ask the assistant to fix them, simulating a simple feedback loop. This shows how AI can iterate on its code based on your input.
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 the main benefit of a closed-loop harness?
- The main benefit is improved reliability and accuracy, as the AI agent can identify and correct its own mistakes before completing a task.
- How does a closed-loop harness work?
- It works by allowing the AI agent to review its own output, compare it to the desired goal, and make adjustments or corrections as needed.
First seen on Subvolts Apr 3, 2026. Updated Oct 7, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.