惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Apple Machine Learning Research
Apple Machine Learning Research
aimingoo的专栏
aimingoo的专栏
H
Help Net Security
腾讯CDC
T
Tailwind CSS Blog
Hugging Face - Blog
Hugging Face - Blog
人人都是产品经理
人人都是产品经理
酷 壳 – CoolShell
酷 壳 – CoolShell
MongoDB | Blog
MongoDB | Blog
宝玉的分享
宝玉的分享
有赞技术团队
有赞技术团队
美团技术团队
雷峰网
雷峰网
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
博客园 - 司徒正美
博客园_首页
Recent Announcements
Recent Announcements
云风的 BLOG
云风的 BLOG
B
Blog RSS Feed
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
D
Docker
博客园 - Franky
Jina AI
Jina AI

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
How to stop an AI agent from burning $47,000 in a loop no...
Brian Hall · 2026-06-23 · via DEV Community

A multi-agent research system sat in production for eleven days doing exactly what it was built to do. Four agents, LangChain-style, coordinating over A2A to pull market data and summarize it. Every health check passed the whole time. No crash, no 500, no timeout... from the outside the system was perfectly healthy.

Two of the four agents had quietly locked into a recursive loop, passing clarification requests and verification instructions back and forth, thousands of times, around the clock. Nobody noticed because nothing was technically wrong. The thing that finally caught it was a person opening the invoice and asking why the number was so high. The number was $47,000.

That story has been making the rounds because it's so relatable, but it isn't a one-off. Uber said it burned through its entire 2026 AI coding budget in four months. One company reportedly ran up a $500M Claude bill after rolling out access with no usage caps. The FinOps Foundation said that around April, the conversation across the industry flipped from "go fast" to "we need guardrails, how do we control this." This is the dominant operational failure mode in production agents right now, and it's barely about the model at all.

Why this keeps happening

The reason isn't that people are careless. It's that the spend cap, when it exists at all, lives in the wrong place.

Most teams "control" cost with monitoring. A billing alert. A dashboard. Maybe a daily spend report. But every one of those is a postmortem. By the time the alert fires, the money is already gone. The $47K loop didn't trip anything because the team was watching user metrics, signups, queries completed, response quality, not per-agent spend in real time. The bill was a monthly line item, not a guardrail.

And the other common fix, putting a budget check inside the agent, has its own problem: the agent is the thing that's misbehaving. A loop that's lost the plot isn't going to cleanly evaluate its own "am I allowed to keep going" check. You're asking the runaway process to stop itself. Sometimes it does. The eleven-day ones are when it doesn't.

There's also the part nobody likes to admit: nothing here was broken. The agents were "working." That's exactly why it ran for eleven days. A failure that looks like normal operation won't get caught by anything watching for failures.

The fix is a hard ceiling that lives outside the agent

The cap has to sit somewhere the loop can't reach, and it has to fire before the call, not after the invoice. That means a budget that's enforced on the tool call itself, not advice in a prompt and not an alert after the fact.

This is the part I work on, so here's how we do it in Faramesh. Your whole policy lives in one file, and a budget is a few lines:

agent "research-crew/analysis" {
  default deny

  rules {
    permit market_data_lookup
    permit summarize
  }

  rate_limit "market_data_lookup": 30 per minute

  budget daily {
    max       $50
    on_exceed defer
  }
}

Two things are doing the work. The rate_limit caps how fast a single tool can be hit, so an agent stuck in a tight loop can't fire the same call hundreds of times a minute. The budget block puts a hard daily ceiling on spend, and on_exceed defer means that when the ceiling is hit, the next call doesn't run, it pauses and waits for a human. The loop stops at the dollar, not at the invoice.

The important part is where this runs. Faramesh sits between the agent and its tools as a local daemon, and every tool call goes through it before it executes. The decision is deterministic, there's no model in that path, so the same spend under the same policy always gets the same answer. The runaway agent can't talk its way past it, because the check isn't inside the agent. It's a wall in front of the tool.

On the $47K incident specifically: a daily cap would have turned an eleven-day, $47,000 silent loop into a one-day, $50 pause and a notification. Same loop. Same bug. Wildly different outcome, because the ceiling didn't depend on anyone noticing.

The takeaway

Runaway cost isn't really a model problem and it isn't bad luck. It's an architecture problem. The spend limit is usually in a place the spend can route around, inside the agent, or after the fact in a billing alert. Move it outside the agent and in front of the action, and the worst case stops being "we found out when the invoice came" and starts being "it paused and pinged us."

Faramesh is open source. If you want to put a hard ceiling in front of your agents, the repo's at github.com/faramesh/faramesh-core. If you wire it up and something's off, tell me, that's all useful feedback right now.