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

Y
Y Combinator Blog
博客园_首页
雷峰网
雷峰网
V
V2EX
博客园 - 司徒正美
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
月光博客
月光博客
Hugging Face - Blog
Hugging Face - Blog
WordPress大学
WordPress大学
T
Tailwind CSS Blog
小众软件
小众软件
博客园 - 叶小钗
美团技术团队
酷 壳 – CoolShell
酷 壳 – CoolShell
Apple Machine Learning Research
Apple Machine Learning Research
IT之家
IT之家
MyScale Blog
MyScale Blog
Blog — PlanetScale
Blog — PlanetScale
大猫的无限游戏
大猫的无限游戏
Jina AI
Jina AI
人人都是产品经理
人人都是产品经理
H
Help Net Security
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻

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
AI Agents Are Lying to You
Bryan | · 2026-05-15 · via DEV Community

 Every AI coding tool on the market has the same pitch. Describe what you want and we'll build it. Cursor, Copilot, Devin. They all promise autonomous code generation. And they all have the same problem.

You can't verify what they did.

They generate code. Sometimes it works. Sometimes it doesn't. But you never actually know why it worked, what decisions were made along the way, or whether the output matches what you asked for. You're trusting a black box with your codebase.

That's not autonomy. That's hope.

The Verification Problem:
Here's what happens when you use a typical AI coding agent. You write a prompt. The agent generates code. You read through it, maybe. You ship it, probably.

That third step is where everything falls apart. You're reviewing AI generated code with human eyes, trying to catch mistakes in logic you didn't write. It's like proofreading a legal contract in a language you half speak. You'll catch the obvious errors. You'll miss the ones that matter.

And the agent won't tell you what it got wrong. It can't. It doesn't have a verification layer. It generated output and moved on. There's no audit trail. No execution log. No proof that the code it wrote actually satisfies the intent you described.

If you can't audit it, you don't own it.

Context Blind Execution
The deeper issue is context. Current AI agents operate without persistent awareness of what they've already done, what failed, or why. Every prompt is a fresh start. Every session is amnesia.

The same mistake gets made across runs because there's no memory of past failures. There's no way to trace why a decision was made three steps ago. When something breaks, you're debugging code you didn't write with zero execution history.

It's not that these tools are useless. They're genuinely fast at generating boilerplate. But speed without verification is just technical debt with extra steps.

What Verifiable Execution Looks Like
I'm building BuildOrbit to solve this. It's a verifiable execution runtime for AI agents. Every action the agent takes is logged, traceable, and auditable.

The architecture is built on three layers of truth.

Intent Truth. What you actually asked for. Your prompt is parsed into a structured intent that becomes the canonical reference for the entire run. Not a suggestion. A contract.

Execution Truth. What the agent actually did. Every phase of the pipeline is recorded. What code was generated, what decisions were made, what was verified and what wasn't. This is the authoritative record. If there's a conflict between what the agent said it did and what actually happened, the execution log wins.

Reality Truth. What actually shipped. The final deployed state is compared against intent and execution. Did the output match the request? Can you prove it?

Each layer checks the others. The agent can't silently hallucinate a feature, skip a requirement, or paper over a failure. If something goes wrong, you know exactly where, when, and why.

Why This Matters
This isn't academic. If you're building anything real with AI agents, anything that touches production, handles user data, or needs to work reliably, you need to be able to answer one question.

Can you prove your agent did what you asked?

Right now, with every major AI coding tool, the answer is no. You can look at the output and guess. You can run tests after the fact. But you can't trace the decision chain from intent to execution to deployment.

BuildOrbit makes that traceable. Every run produces a complete audit trail. When something fails, you see the phase it failed at, the reasoning the agent used, and the exact point where execution diverged from intent.

No black boxes. No blind trust. No "it works on my machine."

The Honest Version
I'm one person. BuildOrbit is pre revenue. I don't have a team or a Series A or a wall of testimonials. I'm building this in public because I think the problem is real and the current solutions aren't solving it.

I'm not claiming to have reinvented software engineering. I'm saying that if we're going to let AI agents write our code, we should at minimum be able to verify what they wrote and why.

That bar is shockingly low. And almost nobody is clearing it.

If you want to see it in action: buildorbit.polsia.app