#Harness

DeepSeek Harness is now available in developer preview to developers building agent harnesses worldwide, with the source code released at the same time. Every agent capability is implemented as a plugin that can be swapped or recomposed.

Prime Agent is our open-source, self-improving coding harness built around two abstractions: the Recursive Language Model (RLM) and the Continual Harness. With Opus 5, it achieves 95.5% on ARC-AGI-3, surpassing the reported human expert baseline.

How Pi's minimal harness improves coding-agent cost and performance, with examples from Databricks and Shopify's pi-autoresearch extension.

The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965), where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself.…

Kimi K3 is the world's first open 3T-class model — frontier performance across coding, knowledge work, and reasoning, with native multimodality and 1M context.

I've been thinking about how to free LLMs from the chat pane for a few years now. Watching what people have tried and seeing what worked (and didn't), I've formed my own opinions on the right way to approach it.…

Learn what an agent harness is, why it matters for production AI systems, and how to implement one step-by-step using Microsoft Agent Framework's create_harness_agent — with real Python code, architecture diagrams, and deep technical walkthroughs.

Summary Anthropic ran a controlled experiment: Opus 4.5 solo ($9) = 20% success. Add a...

We kicked off our new weekly series This Week in AI on Monday, and we covered a lot of ground in 30 minutes, including an AI model that found security holes faster than decades of human auditing, a data center in Utah the size of two Manhattans, and a…

A framework for drawing security boundaries in agentic architectures. Most agents run with zero isolation between the agent and the code it generates. Learn where to draw the boundaries, from secret injection to full application sandboxing.

This article was originally published on Addy Osmani’s blog. It’s being reposted here with the author’s permission. Roughly: Anytime you find an agent makes a mistake, you take the time to engineer a solution such that the agent never makes that mistake…

Background In the age of AI, how do we hire the right people? You don't want to end up...

This Sunday the annual ILT Race Day will take place at Ascot Park Raceway in Invercargill. Hosted by the Invercargill Harness Racing Club, the day...

An arXiv paper renamed a folder I'd been mismanaging all year. Here's what 'Natural-Language Agent Harnesses' (2603.25723) actually changed in my Claude Code workflow.

From Dev.to - docker: 🚀 Taming the AI: I built a Self-Healing SysAdmin Agent in a Docker Sandbox 🛡️

From Dev RSS Feed: Building a Real AI Harness: Auto-Reviewed PRs, Self-Healing Ops, and Non-Engineer Contributors (Series Intro)

From Dev.to - ai: Stop Building Demos: Why 'AI Harness' Engineering is the Secret to Production-Grade LLMs

ReFlect wraps any LLM with deterministic error detection and recovery at inference time. Hands-on guide with code and benchmark results from arXiv:2605.05737.

Claude Code's six-layer architecture uses a 3-layer context compressor at 92% threshold and Redis-based multi-agent FSM protocol. The model is just on

If you've spent any time building with AI lately, you've probably heard the word "agent" thrown...