{
  "id": 5803872,
  "title": "Why LLMs Should Never Touch Money Directly: Building an AI Payment Recovery Agent Solo",
  "url": "https://urgent.news/2026/09/05/why-llms-should-never-touch-money-directly-building-an-ai-payment",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-05T17:33:03.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/itssubhamoy/why-llms-should-never-touch-money-directly-building-an-ai-payment-recovery-agent-solo-493o"
  },
  "original_language": "en",
  "account": "The Razorpay AI Buildathon 2026 presented a challenge for the solo developer who wanted to create something beyond a simple chatbot utilizing an LLM API. The developer found particular interest in addressing the issue of what to do after a payment fails. It is not always the case that such a failure results in lost revenue; sometimes, it could be due to temporary bank issues, expired cards, or customers needing alternative payment methods. Even when a payment seems unsuccessful, there might have been a successful transaction with an ambiguous response.\n\nThe key challenge lay in determining when to retry a transaction, when to refrain from retrying, and when to involve a human. The difficulty came from the need to avoid accidentally charging a customer twice. This led to the creation of REVA - Razorpay Revenue Recovery Agent.\n\nThere were two primary approaches to handling payment failures. The first was blind retries, where the system would retry failed transactions according to a predetermined schedule, without considering the reason for failure. This method might result in unnecessary retries and, more critically, increase the risk of duplicate charges when the payment state is uncertain. The second approach involved creating a large set of if/else conditions, which, while more predictable, proved difficult to maintain and inadequate for handling contextual decisions.\n\nThe developer aimed to find a middle ground. They desired an agent capable of reasoning about the situation while ensuring that deterministic guardrails prevented any violation of critical financial policies. The original idea was to have a system where payment failures would trigger an error to an LLM, which would then decide the appropriate course of action before retrying the payment.",
  "summary": "How I built REVA for Track 3 of the Razorpay AI Buildathon 2026—and why deterministic guardrails matter more than AI autonomy when money is involved. The Problem I Wanted to Solve Building for Track 3 of the Razorpay AI Buildathon 2026 as a solo developer came with one obvious challenge: There were a lot of talented engineers building impressive things. I didn't want to build another chatbot with…",
  "key_points": [
    "Developer sought to create AI solution for payment failures beyond chatbot",
    "REVA (Razorpay Revenue Recovery Agent) designed to reason payment situations",
    "Agent balances retry decisions with financial policy guardrails"
  ],
  "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."
}