Your harness, your memory

arXiv:2604.21003v1 Announce Type: new Abstract: AI agents are increasingly deployed on complex, domain-specific workflows -- navigating enterprise web applications that require

<p>Most developers spent 2023 and 2024 experimenting with AI-assisted code completion and chat interfaces. Those tools were useful—but they were also passive. You typed a prompt.

<p><strong>A Harness for Every Task: Dynamic Workflows (dynamic work orchestration) in Claude Code – How to Solve Complex Tasks with Tailor-Made Multi-Agent Systems</strong></p>

<p><em>By Vivek Trivedy, Product Manager @</em> LangChain</p><div class="kg-card kg-callout-card kg-callout-card-blue"><div class="kg-callout-emoji">&#x1f4a1;</div><div

<!-- SC_OFF --><div class="md"><p>I read the latest Anthropic blog post with great interest. How can I setup a similar harness?</p> <p><a

<p>Birgitta Böckeler, writing about harness engineering at Thoughtworks, draws a distinction that turns out to be more useful than it first appears: a harness contains

arXiv:2609.30967v1 Announce Type: new Abstract: Most work on improving large language models treats accuracy as the sole objective. We argue that the harness, the Python code

New research on why prompts, self-evaluation, and context management fail in production agents, and which of those jobs belongs to the runtime instead.