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

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
MyScale Blog
MyScale Blog
U
Unit 42
M
MIT News - Artificial intelligence
小众软件
小众软件
P
Proofpoint News Feed
雷峰网
雷峰网
L
LangChain Blog
S
SegmentFault 最新的问题
腾讯CDC
F
Fortinet All Blogs
A
About on SuperTechFans
WordPress大学
WordPress大学
Vercel News
Vercel News
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
G
Google Developers Blog
大猫的无限游戏
大猫的无限游戏
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
D
Docker
N
Netflix TechBlog - Medium
Apple Machine Learning Research
Apple Machine Learning Research
Recent Announcements
Recent Announcements
D
DataBreaches.Net
Stack Overflow Blog
Stack Overflow Blog

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
The AI FOMO Trap: Why your Multi-Agent System is brittle ...
Glendel Joub · 2026-05-14 · via DEV Community
Cover image for The AI FOMO Trap: Why your Multi-Agent System is brittle (and how to fix it)

Glendel Joubert Fyne Acosta

A developer on Reddit recently told me: "Companies right now are risking the LLM-led parts of their architecture due to FOMO. We'll see how far they get".

He is absolutely right. Fear Of Missing Out is driving engineering teams to ship "Autonomous Agents" at breakneck speed. But in the rush to production, we are abandoning 20 years of established software engineering principles.

We are letting probabilistic models control deterministic runtimes.

If you are routing network traffic, validating data schemas, or checking user permissions using an LLM prompt, you are not building a resilient system. You are building a fragile prompt-chain wrapped in hope. When it fails (and it will), it will be slow, expensive, and completely un-auditable. InfoSec won't accept "the model hallucinated the auth check" as a valid incident report.

The Cure: The Manager-Executor Pattern

To build enterprise-grade Multi-Agent Systems, we must separate the Cognitive from the Deterministic.

1. The Manager (Probabilistic) This is the LLM. Its only job is to reason, plan, and analyze context. It decides what needs to be done. It does not execute code. It does not manage its own memory. It requests actions via strict JSON schemas.
2. The Executor (Deterministic) This is your runtime framework. It acts as the boundary. When the Manager requests an action, the Executor:

  • Verifies the agent's permissions.
  • Validates the payload against a strict schema.
  • Checks the token/cost budget.
  • Executes the code (API call, DB write).
  • Returns the exact result to the Manager.

The Framework Controls the AI

The fundamental shift required in MAS architecture is understanding that the framework must control the LLM; the LLM must never control the framework.

Right now, developers are having to build these custom state machines and validation layers from scratch because popular frameworks default to LLM-routing. It's time we standardize this. We need "A Real Framework" for Multi-Agent Systems—a framework that enforces the Manager-Executor pattern by default.

Stop relying on vibes-based engineering. Let's get back to rigorous software architecture.