{
  "id": 2866924,
  "title": "Why this blog exists",
  "url": "https://urgent.news/2026/08/23/why-this-blog-exists",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-23T20:17:20.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/loopandretry/why-this-blog-exists-37pe"
  },
  "original_language": "en",
  "account": "This blog, Loop & Retry, serves a specific purpose in the world of building language model (LLM) agents for production. Many existing writings on the subject are either one-off demos that work in a single instance or posts predicting a technological singularity within a few months. This blog aims to fill the gap between these two extremes, focusing on the more realistic scenario where an agent is shipped and deployed, only to encounter unexpected issues later on.\n\nThe author's main concern is the tendency for agent content to be mistaken for a functioning system when in fact it's only a working demo. A demo only needs to succeed once, while a system must be able to handle thousands of potential failures, including malformed inputs, rate limits, tool errors, and retries that may exacerbate the problem. The underlying principle guiding the blog is straightforward: every post should demonstrate the version that fails and the subsequent fix. All numerical data should be measured or cited, without relying on fabricated benchmarks. If a claim cannot be validated through reviewing the actual code, it should not be published.\n\nThe blog is structured around six key pillars: context engineering, tool design, evaluations, cost and latency, failure modes, and agent architectures. The first area to be explored in depth is retry budgets, specifically why a 20% per-step failure rate quietly leads to a doubling of token costs. Upcoming posts will aim to cover these topics approximately once or twice a week, with new content published via RSS for subscribers interested in this niche area of LLM agent development.",
  "summary": "Originally published on Loop & Retry — field notes on building LLM agents that survive production. Most writing about LLM agents is either a demo that works once on stage or a thread promising the singularity by Q3. This blog is for the gap in between: the part where you ship an agent, it survives contact with real inputs for a while, and then it does something expensive and stupid at 3 a.m.…",
  "key_points": [
    "Loop & Retry blog fills gap between demos and tech singularity claims",
    "Author focuses on realistic agent deployment with unexpected issues",
    "Posts demonstrate failing version and subsequent fixes with measured data"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}