I Built a Debugger for LLM Agents — Here's Why "Observability" Wasn't Enough

<p><strong>TL;DR:</strong> I built the same browser agent twice — once with 500 lines of Python, once with 7 lines of JSON. The second one took 5 minutes. The agent harness layer

<p>Last week my research agent failed at step 15 of a 30-step run. Langfuse showed me exactly where it broke. The writer sub-agent hallucinated, citing a stale 2019 population

<h1> How I Built a Time-Travel Debugger for AI Agents </h1> <p>What if debugging an AI agent worked more like debugging normal code?</p> <p>Pause execution.</p> <p>Inspect what

<!-- SC_OFF --><div class="md"><p>I am curious how non-coders are currently using frontier ai models and capabilities. Specifically in technical fields.</p> <p>I am a mechanical

<p>Vertex AI has been Google Cloud's AI development platform since 2021. On April 22, 2026, at Google Cloud Next in Las Vegas, Google retired it — not with a deprecation notice,

<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> Anthropic Managed Agents Architecture: Decoupling Brain from Hands </h1> <p>Anthropic published <a href="https://www.anthropic.com/engineering/managed-agents" rel="noopener

<p>I had an idea, and it starts from a habit.</p> <p>LLM agents are genuinely good at <strong>static analysis</strong>: give them a backtrace and<br /> the source around it, and