A Black-Box Framework for Evaluating Trust in AI Agents

<p>Building a machine learning model is relatively straightforward today.</p> <p>You train it.<br /> Evaluate it.<br /> Tune it.</p> <p>Eventually, you get a model that performs

<!-- SC_OFF --><div class="md"><p>I have built a programmable governance layer for AI agents. I am considering to open source completely. Looking for feedback.</p> <p>Agent demos

<!-- SC_OFF --><div class="md"><p>Projects are <a href="https://github.com/snap-research/locomo/issues/31">still submitting new scores on LoCoMo as of March 2026.</a> but the

<p>Every AI agent tutorial follows the same script:</p> <ol> <li>Import LangChain</li> <li>Define some tools</li> <li>Call the LLM in a loop</li> <li>Ship it!</li> </ol> <p>And it

<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>Your agent passed all the tests. It's been running in production for three weeks. And it has been quietly wrong the entire time.</p> <p>This is not a hypothetical. Galileo's

<h1> Google Remy and Meta Hatch: The Technical Architecture Behind 24/7 Personal AI Agents </h1> <p>Two big AI agent stories broke this week that every developer building on top

arXiv:2610.03894v1 Announce Type: new Abstract: A deployed LLM agent emits tool calls, queries, and code that can be silently wrong -- by the time the error surfaces, the action