What people are building with Jev

梅澤 寛太｜Web・業務システム開発

@umezawakanta13

計測画面付きの別試行も補足します（成功3応答）。 中央値： ・ブラウザ往復 1,139.7ms ・Jev HTTP往復 490.3ms ・状態取得→操作 1,139.8ms 今回も最初の敵に接触。HTTP往復には通信時間が含まれ、純粋な推論速度ではありません。遅延と操作判断の影響は引き続き切り分け中です。

uehaj

@uehaj

jeven-doors.uehaj.workers.dev/?g=mm2rc2cp 「Jevの扉」、ジャンルを作成してアップロードする機能をつけました。WebMCP対応なので、エージェントに作成させると便利です。 #Jev #JevenDoors

つぼね👨‍💻

@tubone24

Jevに飽きちゃったのと、全然利用料が安いので、公開してみました。 雨の日はスピードで遊ぼう！！ jev-speed.tubone24.workers.dev

tshmieldev

@tshmieldev

Jev vs LLMS for clasifying tweets in your feed! Try it out: github.com/tshmieldev/sha… Make sure to drop a star!

Shashank Jha

@theshajha

if you think you have seen it all with jev (@typesafeai), here's something new - play chess against jev

MD Fazal Mustafa

@the_mdfazal

🚨 A LOCAL 421M MODEL JUST ATE CLOUD JEV ON SPEED Laya is an open-source System 1 decision model that runs on your machine. Laya: 86.5 decisions/sec, P50 ~9ms, score 46 Jev: 3.2 decisions/sec, 317ms API round-trip, score 1 Ships with: — 421M params — ~1GB inference memory — Millisecond local calls, no network — Typed decisions in one forward pass — Apache-2.0 and self-hosted While the cloud waits, local decides.

Baris Terzioglu

@terzioglubrs

Agent memory is usually just an append-only Markdown file that grows forever, and most frameworks load the whole thing into context on every single run. That has been bothering me for a while, but never quite enough to fix it for our agents. Jev feels like it might be a low effort patch to this, by simply scoring each memory against the prompt first, then load only the relevant bits for that specific request.

Petru - Tech Driven

@techdrivenpetru

Yes but not with Jev. I used another classifier, locally, just as fast, just as good. Example: setup a "server" in python that loads the classifier model. Add a hook in Claude that fires on "pre-tool-use" and next time you ask Claude a random question like "how do I lint check a project?" and it tries to freelance and read your entire repo only to intoxicate itself and pollute its context, the classifier will slap its hand, say "no sir, you answer from knowledge" and deny the tool call. I tested this yesterday with success, but need to refine it as it misfires. Basically I was able to identify general queries, instances where I would ask something and Claude would rush ahead and run pip install without me asking or just write code instead of answering. A classifier is hypercheap compared to a regular LLM and would catch all of these. The model I used was DeBERT large. It's still stupid fast, I tested it on an Apple with M1 (regular) and you don't feel it running.

たてこ｜AI好き社会人

@tateko_ai

今日のgakuse.aiの勉強会テーマは「Jev」 Jevの基本的な仕組みとか、海外での活用事例など、Jevでできることの広さを知れてめっちゃ勉強になったな！ 特に印象に残ったのは、ハーネスにJevを組み込むことで、トークンを効率的に使えるという話 早速Jev使って人狼ゲーム作ってみた！

tanav

@tanavtwt

Created a slop detector extension with Jev It scans all the post on the screen in the real time and classifies it on categories like scam, slop, clean, etc. Shows a minimal badge on the post with the confidence score. Comment bellow if you want to try the extension.

たく｜ガチのCopilot達人

@taku_ai_case

これはめちゃくちゃ勉強になりました。 Jevに回答させるのではなく、CodexやClaude Codeへ渡す記憶候補を選ばせる。文章を書けないAIの特性を、ここまで実用的な仕組みに落とし込む発想がすごいです。

同語 Jackson

@t0ng7u

Jev is live in New API now! Choice. Score. Noul. Structured judgments that drop straight into code. Same TypeSafe SDK. Point it at your New API gateway. One plugin. No rewrite. newapi.pro/zh/plugins

Manoj Rajendiran

@svencreations

Creators saying their new tool “killed” another is understandable. It’s marketing and rage bait. But people retweeting it without even trying the tool? That’s the worrying part. I’ve seen at least 10–20 “Jev killers”(@typesafeai) on my timeline already. Tried a few myself. Most weren’t even close. We’re amplifying opinions before forming our own.

スナガク | Codexではじめるエージェンティックコーディング

@suna_gaku

一日遅れですが、Jevを使ったアプリを作ったので共有します！ Xに投稿する前に、「彼女に見られても怒られないか」を判定してくれるアプリです。喧嘩を未然に防止するため、ぜひチェックを挟んで下さい！ アプリ URL sunagaku.com/labs/kanojo-ch… X の元投稿 x.com/suna_gaku/stat… #aimeetup

Prasenjit Sarkar

@stretchcloud

I keep seeing the same fix across agent stacks this week: teams are ripping the expensive model out of the middle of their decision loops. TypeSafe shipped Jev in early access on September 15. Not a chat model. They call it a System One model: hand it a state and typed options, it returns calibrated probabilities, no text generation at all. The mechanism is simple. Most agent loops burn a full LLM call on decisions that never needed generation: route to worker A or B, is this result relevant, approve or block this action. TypeSafe claims up to 200x faster inference and 400x lower cost on that class of decision. Ricker's own tests below land at 193x and 444x. What happened next is the real story. Within four days, Cognition's Jared Palmer shipped Kev, a LoRA adapter on Qwen2.5-0.5B trained in 1 hour 45 minutes on a MacBook Pro, with an API close enough to point TypeSafe's own SDK at it. Kev now scales up to a 9B version that trails Jev by about 4.5 points on held out evaluation. Laya-MLX arrived the same week targeting millisecond decisions on Apple Silicon. A community leaderboard, JevBench, already ranks a dozen of these models. The adoption signal convinces me this is not a toy. TanStack AI shipped a native decide() API for typed choices, scores and booleans. Beacon, an open source memory layer, uses Jev to score which coding sessions are worth turning into reusable lessons. Three teams, one primitive, inside a week. RouteLLM out of Berkeley showed back in 2024 that routing simple queries to a cheap model cuts cost over 85% while holding 95% of GPT4 quality, and production semantic routers report 40 to 90% savings today. What changed is that the router stopped being a side project and became a shipped, benchmarked model category with a name. The bottleneck this solves is real: every agent framework has a model sitting in a loop answering questions that never needed a sentence back. My read is that the decision layer becomes as standard a piece of the agent stack as the vector database became for retrieval, and whoever owns the default there owns a lot of the unit economics conversation for the next year of agent infrastructure. https://x.com/0xRicker/status/2101705843200721203

Stas Kulesh

@staskulesh

Added shareable game result to jevchess.com Jev won again, but. The robots are coming.

Stanislav Sorokin

@stas_sorokin_

1,000 AI papers sorted into 24 topics for $0.0585. Then Opus 5 graded the labels. @nutlope's Jev paper map went viral, but the pipeline never shipped and the eval was "still running". So I rebuilt both and opened them. The first judge run came back empty: Opus spent its whole budget thinking and answered nothing. Reasoning off, second run: it agreed with Jev on 85 of 100 papers, at 153x the cost and 1.9s against 57ms per paper. The 15 misses are not random. One number Jev already returns tells you which labels to recheck. Cheap models sort. Expensive models audit only what the cheap one flags. Repost if you classify anything at scale, because the eval rows are public and anyone can rerun them with their own judge in one command. Code in the reply.

そら ☁️ AgentSwarm 自動化オタク📱

@sora19ai

今週のgakuse.aiの勉強会はもちろん 「Jev」！！！！！！ ・他のLLMと何が違うのか ・どんなところで使えるのか ・Agent Harnessにどう組み込むのか などなど色々議論できました👇

Satoshi Nagayasu 🧠🤖

@snaga

TypeSafe System One（Jev）によるHacker Newsパーソナライズ推薦の実験と複合判定アーキテクチャ gist.github.com/snaga/12c62ad5… 毎朝、Hacker NewsのチェックをAIエージェントでやってるんだけど、自分向けのレコメンドをJevで組んでみた。 なるほどー、という感じである。楽にはなりそう。

Artem

@sl1ma4

🔥 looks like Jev just got an open-source alternative. Laya is a 421M parameter model built for making decisions instead of generating text. the interesting part: - runs locally on your laptop or phone - uses less than 1GB of memory - supports structured decisions (yes/no, scoring, multiple choice) - free + open source in a 30-second Snake benchmark against cloud-based Jev: Laya: 86.5 decisions/sec, score 46 Jev: 3.2 decisions/sec, score 1 the catch? Laya has a 512–1024 token context window and doesn't generalize as well as Jev without additional tuning.

Simplifying AI

@simplifyinAI

so we already have an open source alternative to jev... and it's 6-7x faster?! it's a typed-decision classification system: no chat, no generated text, just fast yes/no, scoring, or choice answers. > runs in under 1gb of memory > free on hugging face > runs on a laptop, or even a phone there's a real demo where it plays snake live, making a fresh decision every single move. two honest limits, straight from the project itself: > only 512-1,024 token context, some cases won't fit > weaker generalization than jev out of the box

Akash Jain

@akashpurjalkar

Jev is playing Minecraft. I built a bot powered by Jev that can construct houses, towers, lakes and castles on command. It also fights mobs, uses a sword, and tries to dodge skeleton arrows in real time. Still rough, but genuinely fun to watch. @typesafeai Jev

内田勉 DirecTune.app β公開中

@sidodtv

9/23開催 #生成AIなんでも展示会 Vol.6 のお品書きできました！ サークル名「他人ハウス」。場所はI-12です 内田：JevとLaya使用・超高速フットサルシミュレーター ころとろ：しまむらポエムマシン 場所は浜松町、入場無料、現在1400人が参加予定。みんな来てね！

内田勉 DirecTune.app β公開中

@sidodtv

なんか24時間後に Tiboリセットくるらしいので、今のうちに #directune の開発を進めるよ〜！ まずは、ゆっくり風動画解説で背景が自動作成されるようになったよ。試しに Jevの解説動画を作ったよ DrecTuneは、AIがアシストしてくれる動画生成・制作サービスだぞ‼️

Coach Shweta Bajaj

@shwetabjaj

This Jev experiment is fun, but the reliability question is actually the interesting part. Snake is simple enough to understand, but unforgiving enough to expose bad decisions fast. Play alone, play with Jev, veto its moves, or race it. A playful way to test decision quality, corner after corner. 🐍 @typesafeai @CarolMonroe http://jevplayssnake.lovable.app

Coach Shweta Bajaj

@shwetabjaj

Jev Skill Suggestion for Claude Code is a smart idea. Instead of loading every skill into context, Jev decides which one is actually relevant and injects only that. My takeaway: better context hygiene, less clutter, and potentially more efficient agent workflows. @typesafeai @vercel

Dibya.shree

@shree_code

Jev doesn't write. It only decides. Yes/no, choices, scores — each with calibrated confidence in 70–500ms. Input is $0.042 per million tokens. Output is free. The first System One model. This is the plumbing agents were missing. firecrawl.dev/blog/what-is-j…

Shengkun Ye

@shengkunye

Introducing Jev + OpenRouter + tools. You can now use Jev with 2,000 tools on Monid. Your agent picks the tools, Jev speeds the work up 30x. > Score 2,000 leads > Scan the TikTok viral video hooks > Research fundraising of 200 companies > Audit a site's SEO and rebuild the internal links on every page > Sort every Reddit thread about your category

SCRAPS

@scrapsonsolana

For marketing $SCRAPS, I’m trying something I haven’t seen anyone else do. I’ve been experimenting heavily with the latest AI tools (JEV from @typesafeai), burning through 178M+ tokens and nearly 48,000 requests testing different ideas. One of those experiments is focused entirely on marketing: finding the fastest path to high engagement and the best ROI possible. Now that SCRAPS is live on Google Play, it’s time to put it to the test. The experiment starts now.

さっと

@satto_sann

Jevでサクッと分類器は価値があるのかビルドトラップできそう

AMIT RAWAT

@sahajamit

I built a Chrome extension that judges every post in my feed before I reach it. READ, MAYBE or SKIP, in about 300 ms, using Jev by @typesafeai. No scripted rules, no selectors, no LLM. Real time, my X feed:

Andrew

@s4yonnara

Jev CEO, Diogo Almeida (ex-OpenAI): "It's not Claude Code because Claude Code is still part of the assistance era." He co-authored GPT-4, ChatGPT, InstructGPT and RLHF. Now he's building what comes next: 200x faster. 400x cheaper. A decision brain for AI agents. His argument is simple: frontier LLMs are insanely good at creating, but we're wasting them on thousands of tiny decisions: Which agent goes next? Is this relevant? Does this need approval? Those calls don't need generation. They need a model built to decide. In 18 minutes, Almeida explains why today's AI was built for assistance, not real automation, and why "tomorrow's AI will be for automation." This makes most $500 agent engineering courses look obsolete. Watch it today, then steal my 10-step guide to giving your agents a decision brain below ↓

s1rozha1

@s1rozha_

Jev hype is not about chat. it’s about decisions everyone is trying to compare it to ChatGPT / Claude / Cursor wrong frame Jev doesn’t need to write you a beautiful paragraph it needs to answer questions like: should this agent act now? which tool should run next? is this signal worth trading? is this market mispriced? should this wallet be ignored or watched? that is the interesting part prediction markets are basically endless small decisions under uncertainty not one giant “AI intelligence” moment thousands of tiny calls: price changed liquidity moved wallet entered news dropped odds lagged edge appeared edge disappeared you don’t want a slow model writing essays for that you want something cheap, fast, typed, and measurable that’s why Jev is interesting. not because it replaces frontier models because it makes the boring decision layer cheap enough to run everywhere

AIセラ｜Obsidian✖️クライアントワークを極めるエンジニア

@ryo_hukugyo_pro

Jevをいかに使いこなすかが今後トークン節約や自動化をするAI社員を大量量産する上で、 めちゃくちゃ重要。

Richard Seroter

@rseroter

I'm reading up on Jev (typesafe.ai/blog/introduci…) and learning how it differs from "regular" classification ML models. I gave a bunch of articles to @googlecloud Gemini Enterprise and asked it for a breakdown. Helpful. Still digging in before going hands-on.

Robert Youssef

@rryssf

all things Jev in one place a GitHub collection just dropped packed with hundreds of tools, libraries, and projects built on top of Jev, from teams treating decision-making as its own primitive, separate from language generation entirely. github.com/AnotiaWang/awe…

Robotics Alpha

@robotics_alphax

Strike Robot wiring Jev into its Physical AI stack is not a model announcement or a new UI layer. it is an autonomy architecture shift. the signal that usually separates real autonomy from scripted behavior is where the decision loop lives. when a robot queries a remote model for every action, the latency budget is visible in the motion. there is a pause between stimulus and response that teleoperation hides but autonomous operation cannot. what you want is local inference for routine actions and remote calls only when confidence drops. staging tells are easy to spot if you know what to look for. when Jev is actually routing decisions, the robot will execute most movements without a cloud round trip. the moment to watch is at decision boundaries. an unexpected obstacle, a grip that slips, a path that changes mid stride. if the system freezes or the motion cadence stutters, the model just got called. if it flows, Jev stayed local and the confidence threshold was high enough. latency in continuous runs is the honest signal. the gap this closes is always-on large-model inference burning compute on decisions that a smaller local model handles with 95% confidence. a credible in-the-wild demo would show the robot moving fluidly through a routine task, hitting an edge case, pausing once while Jev fires, then resuming without a second stall.

Richard Meng

@richard_meng_01

Nitpicky, an AI generated photo detector powered by jev AI generated photos can be told from nits. That's why we build something to zoom into every detail: faces, fingers, characters, numbers, poses, where common senses fall apart, judged by jev

TechVerser

@realfxw

Claude Code 的上下文终于不用再被一堆预加载技能塞满了！推荐一个非常优雅的按需加载解决方案：Jev Skill Suggestion。 以往在配置大量自定义技能（Skills）时，最头疼的就是 Context Window 膨胀问题。许多低频技能即便一次都用不上，也会在每次对话启动时常驻上下文，不仅白白消耗 Token 成本，还会稀释模型的注意力，甚至引入不必要的提示词干扰。 这个 Mod 的核心逻辑是将技能路由与主体执行彻底解耦（Just-in-Time 动态注入）： 常态零占用：将技能标记为仅用户可调用，默认状态下完全排除在上下文窗口之外，保持 Prompt 绝对干净。 轻量前置路由：用户输入指令后，Mod 会将技能列表交由 TypeSafe AI 的分类器 Jev（或兼容的 Vercel AI Gateway），快速匹配与当前任务最契合的技能。 精准单点注入：只有被分类器判定为高置信度命中的技能，才会在执行当下被动态注入给 Claude Code。 安装仅需一行命令： npx claude-code-templates@latest --mod productivity/jev-skill-suggestion 这种把“工具选择”抽离给轻量分类器、让大模型只专注“任务执行”的架构，正在成为复杂 Agent 系统降本增效的标配设计。随着技能库扩充，未来配合端侧超轻量分类模型（如社区正在探索的 MLX 移植版），整体延迟和隐私表现还会更进一步。 大家目前在 Claude Code 里常驻了多少个自定义技能？这种动态路由机制是否刚好击中了你的上下文焦虑？ #ClaudeCode #AIAgent #AgentSkills #TypeSafeAI #LLM #PromptEngineering #AI编程 #OpenSource #开发者 #独立开发 #全栈开发 #Vercel

RazeDen

@razeden0

i just merge Grok 4.7 + Jev and got the smartest thing i've ever owned grok 4.7 does the thinking. jev does the deciding. together they cost less than my coffee prompt → grok bot → jev asks 6 questions → grok 4.7 opens only the good ones → campaign jev doesn't write anything. it only answers simple questions: yes or no, pick one, or a score. it can't make things up, because it can only pick from answers you allowed i gave it 3,412 leads from x, linkedin and youtube. 6 questions each is 20,472 decisions, done in 15.7 seconds for $0.41 the same reading by hand took me 6h 12m. people say 400x faster. i did the math and it's 1,421x that's $0.00012 per lead, and grok 4.7 only reads the ones worth reading the big mistake: sending everything straight to the new model. you pay the smartest thing you own to say "skip" all day setup took me 9 minutes: key in the secure field, sdk on the agent computer, one router skill, test mode first jev decides, grok bot does the work, and i still press the button on anything i can't undo everyone is talking about the new model. the cheap one deciding what it reads is where the money is

Ranjan Kumar

@ranjankumar

𝐖𝐡𝐞𝐫𝐞 𝐉𝐞𝐯 𝐁𝐞𝐥𝐨𝐧𝐠𝐬 𝐢𝐧 𝐚𝐧 𝐀𝐠𝐞𝐧𝐭 𝐇𝐚𝐫𝐧𝐞𝐬𝐬 (𝐍𝐨𝐭 𝐚 𝐌𝐨𝐝𝐞𝐥 𝐒𝐰𝐚𝐩) Your agent harness needs a decision model. Where you place it depends on one brutal fact: Jev's ordering is trustworthy. Its confidence numbers are not. Most placement guides skip this distinction entirely. They tell you to pick a threshold and execute above it. That works if you actually have a probability. You might not. 𝐓𝐡𝐞 𝐜𝐨𝐫𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦: A model can rank beautifully and lie about magnitude simultaneously. When you gate execution on if confidence >= 0.85: approve_transfer, you are trusting a number that was never calibrated to your prevalence, your cost ratio, or your queue. Move the threshold up and you trade recall you never measured for precision you cannot state. You have no idea how far along an unmarked axis you moved. 𝐓𝐡𝐫𝐞𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬, 𝐭𝐡𝐫𝐞𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐧𝐞𝐞𝐝𝐬: Routing and ranking consume ordering only - which answer is best matters, the number beside it does not. Gated execution consumes magnitude - the threshold is your boundary and it must land on a calibrated scale. Relative logic like if top1 - top2 < 0.1: escalate consumes differences - and rescaling the score axis will flatten margins unevenly, breaking your code. The sepsis alert systems of 2020 learned this hard way. Michigan switched off alerts when COVID shifted patient prevalence beneath a fixed threshold. No weights changed. The denominator moved, the promise broke silently, and nurses drowned in false alarms. 𝐓𝐡𝐞 𝐟𝐢𝐱 𝐢𝐬 𝐬𝐞𝐪𝐮𝐞𝐧𝐜𝐞, 𝐧𝐨𝐭 𝐭𝐮𝐧𝐢𝐧𝐠: First question: is this number admissible as a probability at all? Run a calibration study on a few hundred labelled cases from your own queue. Samuel Sacco's measurements from 18 September and Adil Muhammad Pervez's 8,000 judgments both reach the same conclusion: fit your own map. The weights are shared across every account by design - there is no per-customer adaptation - so a deployer-side calibration map is your only mechanism. 𝐒𝐞𝐜𝐨𝐧𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: given that the number is calibrated, where does your cost ratio put the line? That part of the familiar advice still works. Run them backwards and you are tuning a dial with no markings. Read the full analysis on calibration, harness placement, and where Jev actually fits: https://ranjankumar.in/jev-system-one-model-agent-harness-placement Follow for more practitioner insights on agentic AI systems and production AI engineering. #AgentiveAI #AIEngineering #SystemOne #Calibration #MLOps #DecisionModels #HarnessEngineering #Jev

Raksha T

@rakshaa_t

Jev has some invisible use cases that won’t be clickbaity but hella useful : - auto performance checks on your shipped websites - personal tools like using it for super quick chat compaction - user feedback prioritisation against most relevant data in your company - lots and lots of data sorting - quickly - pairing it with codex / claude to optimise decision making while you code with your agents etc

grahma.dev

@quantium16

既然现在JEV在meta里，我就做了一个网站，让它在里面无限奔跑并收集眼镜，我觉得这个Infinity Runner应该会很高。你们还可以发你们通关的截图，你们中最好的那些人会从这个代币获得supply。 infinity-runner-rho.vercel.app

Doge 🐶

@pprownets2023

💡 ยกระดับ AI Agent ให้เก่งขึ้น 100x ด้วย Jev Engineering (Decision-Making Brain) เคยเจอปัญหาไหม? เวลาทำ AI Agent แล้วสั่งงานด้วยข้อความธรรมดา (Free-form Prompting) มักได้ผลลัพธ์ที่ไม่แน่นอน ควบคุมยาก และนำไปต่อยอดในระบบแบบอัตโนมัติได้ลำบาก 🤯 เอกสาร JEV Engineering ได้เสนอแนวคิดในการแก้ปัญหานี้อย่างน่าสนใจครับ: 🔹 1. Jev คืออะไร? Jev คือ "สมองส่วนตัดสินใจ" (Decision-Making Model) สำหรับระบบ Agentic Systems ที่ไม่ได้เน้นการคุยแบบ Free-form Chat แต่เน้นการแปลงข้อมูลสถานะระบบ (Structured State) ให้กลายเป็น Typed Outputs ที่ชัดเจน เช่น ช้อยส์การตัดสินใจ, คะแนนความเกี่ยวข้อง หรือคำสั่ง ใช่/ไม่ใช่ พร้อมระบุค่าความน่าจะเป็น (Probability) 🎯 🔹 2. ทำไมถึงทำให้ระบบเก่งขึ้น 100x? การแยกส่วน "คิดตัดสินใจ" (Decision) ออกจาก "การลงมือทำ" (Execution) ช่วยลดความคลุมเครือ และทำให้ระบบเสถียรขึ้นอย่างมหาศาล โดย Jev จะส่งผลสูงสุดในจุดที่มีการตัดสินใจถี่ๆ (High-frequency Decisions) เช่น: 🔍 Research Evaluation: ประเมินความน่าเชื่อถือของแหล่งข้อมูลอัตโนมัติ 🛠️ Action Selection: เลือกใช้ Tool หรือส่งต่อ Agent ให้ถูกตัว ✍️ Content & Draft Refinement: วางแผนและเกลาเนื้อหาเชิงลึก 🚦 Human-in-the-loop Gating: ตัดสินใจว่าเคสไหนต้องส่งให้มนุษย์ตรวจต่อ 🔹 3. สรุปสถาปัตยกรรม 3 ขั้นตอน (How it Works) Input State (01): ใส่ Goal, Context, Rules และ Actions ที่ทำได้ Jev Brain (02): ประเมิน เหตุผล วางแผน และคืนค่าเป็น Typed Decision Orchestrator (03): นำผลลัพธ์ไป Validate และ Execute ร่วมกับ Agent, Tool หรือ Human สดๆ 💡 สรุปสั้นๆ: ถ้าอยากให้ AI Agent ทำงานอัตโนมัติได้อย่างแม่นยำ เลิกส่ง Free-form Prompt สั่งงานตรงๆ แล้วเปลี่ยนมาใช้ Structured State + Typed Decisions เพื่อการขยายระบบที่นิ่งและน่าเชื่อถือครับ!