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

D
Docker
小众软件
小众软件
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
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
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
酷 壳 – CoolShell
酷 壳 – CoolShell
Apple Machine Learning Research
Apple Machine Learning Research
月光博客
月光博客
人人都是产品经理
人人都是产品经理
大猫的无限游戏
大猫的无限游戏
V
V2EX
阮一峰的网络日志
阮一峰的网络日志
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - Franky
WordPress大学
WordPress大学
有赞技术团队
有赞技术团队
Hugging Face - Blog
Hugging Face - Blog
Jina AI
Jina AI
博客园 - 聂微东
S
SegmentFault 最新的问题
量子位
宝玉的分享
宝玉的分享
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园_首页

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
Catching the failure is the easy part
Michelle Tristy · 2026-06-15 · via DEV Community

Michelle Tristy

The last post I wrote ended on a loose thread I have not been able to stop pulling at. Almost every memory setup I looked at had a decent answer for what to write down, and almost none of them had a real answer for what to keep. I want to sit with that second half for a while, because the more time I spend with it the more I think it is where the actual difficulty lives.

Start with the part that feels hard but mostly isn't. Noticing that an agent failed at something is close to mechanical. A tool throws an error. A test goes red. A call times out. A change gets reverted twenty minutes after it shipped. You can even catch the quiet ones, the runs where nothing errored but nobody ever confirmed the thing actually worked, by treating "ended without confirmation" as its own small failure. None of this is trivial to wire up, but it is the kind of problem that yields to rules. You can write the rules down and they hold.

So people build the detector, watch it light up, and feel like they have solved memory. They have not. They have solved the easy half and walked right up to the hard one without noticing the seam.

The hard half starts the moment you have a confirmed failure in hand and have to decide what, if anything, it means. A single failure is not one kind of thing. Sometimes it is a fluke, a flaky test or a network hiccup that will never recur and is worth nothing. Sometimes it is a real lesson, a sign that a whole approach is wrong. And sometimes it is just another face of a mistake you already recorded last week, in which case writing it down again only piles more weight onto something you already knew. The detector cannot tell these apart. It only knows that something went red. Sorting which red things deserve to become memory is judgment, and judgment does not collapse into a rule the way detection does.

Then volume shows up and makes it worse. If you keep every failure you catch, the store fills with sediment fast. Someone I talked to for the last post had the agent write a short post mortem after each task, which worked beautifully until there were forty of them and the signal drowned. So you have to consolidate. Merge the near duplicates, summarize the old ones, let the trivial stuff fade out. And consolidation is lossy on purpose, which means every time you do it you are betting on which detail mattered before you actually know. You compress "the deploy failed because the migration ran before the feature flag flipped" down to "be careful with migration ordering," and you have probably thrown away the one specific that would have helped next time. The summary feels tidier and remembers less.

There is a quieter failure mode hiding in here too, and it is the one I find most interesting. When you consolidate aggressively you are tempted to fold the event and the lesson into a single object. What happened, and what you concluded from it, become one note. That is exactly the move that turns memory into superstition. The agent stops holding "this happened once and here is the evidence" and starts holding "this is the rule," and it will defend the rule long after the thing that justified it has changed. A failure that was real on Tuesday hardens into a law by Friday, enforced by a system that no longer remembers why. Keep the event and the conclusion as separate things and you can revise the conclusion later. Fuse them and you cannot.

So what makes keeping so much harder than catching? I think it comes down to signal. Detection has ground truth right when it happens. The test passed or it did not. Keeping has no equivalent. At the moment you are deciding whether a memory is worth holding onto, you usually cannot tell, because the thing that would actually tell you is whether acting on that memory later leads somewhere good or sends the agent back into the wall it already hit. That signal arrives much later, if you capture it at all, and almost nobody is capturing it. We instrument the write and leave the outcome uninstrumented, then act surprised when the store fills with confident junk.

Worth saying plainly: this is roughly how human memory works, and nobody designed that, so maybe it is telling. You do not store your whole day. Something during sleep throws away nearly all of it and keeps a thin, strange, sometimes wrong selection. The recording was never the clever part. The selection is. We have built agents that record fluently and select badly, which is close to the exact inverse of what you want.

I do not have a clean fix, and I am suspicious of anyone who says they do. What I have is a few things I now believe. Separate the cheap detector from the expensive decision, and do not let the first quietly stand in for the second. Do not promote a single failure into a durable rule just because it happened once. Build the cleanup pass in from the start, because the store degrades whether or not you planned for it. And accept that part of the keep decision cannot be automated yet, because the signal it really wants, did acting on this actually work, is one most systems are not even recording.

That last one is the thread I will pull next.