New capability ensures cryptographic trust for AI agents operating in enterprise environments Keyfactor, the leader in digital trust for modern enterprises, today announced a new capability that applies its industry-leading PKI and certificate lifecycle management (CLM) solutions to secure Agentic AI systems. This advancement demonstrates how organizations can extend Zero...
The post <a href="https://ai-techpark.com/keyfactor-validates-pki-based-identity-for-securing-agentic-ai/">Keyfactor Validates PKI-Based Identity for Securing Agentic AI</a> first appeared on <a href="https://ai-techpark.com">AI-Tech Park</a>.

أعلنت شركة Keyfactor عن قدرة جديدة لتأمين أنظمة الذكاء الاصطناعي الوكيلة باستخدام حلولها لإدارة الهوية الرقمية وإدارة دورة حياة الشهادات. يعتبر هذا التطور مهمًا لأنه يعزز الثقة التشفيرية لوكلاء الذكاء الاصطناعي في البيئات المؤسسية ويظهر إمكانية توسيع مبادئ الثقة الصفرية في أطر الأمان التنظيمية.

Keyfactor ha anunciado una nueva capacidad para asegurar sistemas de IA agentiva utilizando sus soluciones de PKI y gestión del ciclo de vida de certificados. Este desarrollo es significativo ya que mejora la confianza criptográfica para agentes de IA en entornos empresariales y ilustra el potencial de extender los principios de Zero Trust en los marcos de seguridad organizacionales.

Keyfactor a annoncé une nouvelle capacité pour sécuriser les systèmes d'IA agentique en utilisant ses solutions PKI et de gestion du cycle de vie des certificats. Ce développement est significatif car il renforce la confiance cryptographique pour les agents d'IA dans les environnements d'entreprise et illustre le potentiel d'extension des principes de Zero Trust dans les cadres de sécurité organisationnels.

Keyfactor has announced a new capability to secure Agentic AI systems using its PKI and certificate lifecycle management solutions. This development is significant as it enhances cryptographic trust for AI agents in enterprise settings and illustrates the potential to extend Zero Trust principles in organizational security frameworks.

Keyfactor Validates PKI-Based Identity for Securing Agentic AI

حقق Google Gemini 3 إنجازًا كبيرًا من خلال تجاوز جميع معايير الذكاء الاصطناعي الحالية، بما في ذلك الأكثر تحديًا. يبرز هذا الإنجاز التقدم الذي حققته فريق الذكاء الاصطناعي في Google ويضع Gemini 3 كمتنافس رائد في مجال الذكاء الاصطناعي. يتم الاحتفال بهذا النجاح داخل مجتمع التكنولوجيا، مما يعكس التطور المستمر لتقنيات الذكاء الاصطناعي وقدراتها.

Google Gemini 3 ha logrado un hito significativo al superar todos los benchmarks de IA existentes, incluidos los más desafiantes. Este logro resalta los avances realizados por el equipo de IA de Google y posiciona a Gemini 3 como un competidor líder en el panorama de la inteligencia artificial. El éxito es celebrado dentro de la comunidad tecnológica, reflejando la evolución continua de las tecnologías de IA y sus capacidades.

Google Gemini 3 a atteint un jalon significatif en surpassant tous les benchmarks d'IA existants, y compris les plus difficiles. Cet accomplissement met en évidence les avancées réalisées par l'équipe d'IA de Google et positionne Gemini 3 comme un concurrent de premier plan dans le paysage de l'intelligence artificielle. Le succès est célébré au sein de la communauté technologique, reflétant l'évolution continue des technologies d'IA et de leurs capacités.

Google Gemini 3 has achieved a significant milestone by surpassing all existing AI benchmarks, including the most challenging ones. This accomplishment highlights the advancements made by Google's AI team and positions Gemini 3 as a leading contender in the artificial intelligence landscape. The success is celebrated within the tech community, reflecting the ongoing evolution of AI technologies and their capabilities.

Google Gemini 3 Just Killed Every AI Benchmark, Including the Hardest of All

Two weeks ago I read a line about tool use with Claude that stuck in my head. Paraphrased:

<blockquote>
Direct tool calls don’t really scale. 
Have the model write code that uses tools, and execute that code instead.
</blockquote>

At the same time, I was knee-deep in wiring a JavaScript execution environment into Contenox, my self-hosted runtime for deterministic, chat-native AI workflows.

So of course the thought was:

<blockquote>
What if I just let the model write the JavaScript and run it inside the runtime? 😅
</blockquote>

This post is about what happened when I tried exactly that.




<h2>
 
 
 What is Contenox?
</h2>

Very short version:

<blockquote>
Contenox is a self-hostable runtime for sovereign GenAI applications. 
It models AI behavior as explicit state machines, not opaque prompt chains.
</blockquote>

Some key properties:

Runtime, not a library, Explicit state machines, Chat-native interface, Vendor-agnostic &amp; self-hosted, written in go with lots of passion and zero tolerance for shortcuts




<h2>
 
 
 The experiment: ask it to fetch and summarize a TODO
</h2>

Once the JS execution hook was in place (a Goja VM with some globals exposed), I wired up a new state machine: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>mux_input → moderate → generate_js → run_js → eval_js_result → (repair_js?) → answer
</code></pre>

</div>



Then I opened the Contenox chat UI and typed:

<blockquote>
Me: “Hey Contenox, fetch the TODO item from jsonplaceholder and summarize it.”
</blockquote>

The first attempt didn’t go as planned.

Instead of a neat summary, I got:

<blockquote>
Contenox: 
“I'm sorry, but it seems there was a problem fetching the TODO item from the provided URL due to an Internal Server Error (status code: 500). Unfortunately, I couldn't retrieve any data at this time. My apologies for the inconvenience!”
</blockquote>

I checked manually – Cloudflare was having a moment, JSONPlaceholder was behind it, and the request really was failing. Okay, fair. Long coffee break.

A bit later, I went back to the same chat and typed:

<blockquote>
Me: “okay let's try again now.”
</blockquote>

This time the reply was:

<blockquote>
Contenox: 
<code>Task titled "delectus aut autem" is incomplete. Nothing to summarize here.</code>
</blockquote>

Wait. It worked? Just like that?




<h2>
 
 
 Under the hood: LLM → JavaScript → tools → LLM
</h2>

Looking at the execution trace made it clear what happened.

<h3>
 
 
 1. Input is normalized and moderated
</h3>

The workflow starts with:

<ol>
<li><code>mux_input</code></li>
</ol>

<ul>
<li>
Parses the incoming chat history into a normalized <code>messages</code> array.

<ol>
<li><code>moderate</code></li>
</ol>
</li>
<li>Uses a small model to classify the input as safe/unsafe.</li>
<li>Output: <code>0</code> → safe → continue.</li>
</ul>

So far this is standard workflow stuff.

<h3>
 
 
 2. <code>generate_js</code>: the LLM writes JavaScript
</h3>

Next, the <code>generate_js</code> task runs with a system instruction like:

<ul>
<li>“You are an expert JavaScript agent for the Contenox runtime…”</li>
<li>“You must output JSON: { "code": "" }”</li>
<li>“You can use httpFetch, executeTask, executeTaskChain, …”</li>
<li>“Script must be synchronous, no async/await, and must return a JSON-serializable object.”</li>
</ul>

Given the chat history, the model produced something like this (simplified): 


<div class="highlight js-code-highlight">
<pre class="highlight json"><code>{
 "code": "var result;
var answer;
var meta = { steps: [] };

var todoUrl = \"https://jsonplaceholder.typicode.com/todos/1\";

// Step 1: Fetch the TODO item
meta.steps.push(\"Fetching TODO item from \" + todoUrl);
var fetchResponse = httpFetch(todoUrl);

if (!fetchResponse.ok) {
 answer = \"I couldn't access that item right now because there was some trouble with getting it. Please check the URL or try again later.\";
 meta.error = {
 message: \"HTTP fetch failed\",
 url: todoUrl,
 status: fetchResponse.status,
 statusText: fetchResponse.statusText,
 body: fetchResponse.body,
 error: fetchResponse.error
 };
 result = { answer: answer, meta: meta };
} else {
 var todoItem;
 try {
 todoItem = JSON.parse(fetchResponse.body);
 meta.raw = todoItem;
 } catch (e) {
 answer = \"I fetched the item, but I couldn't understand its format. It doesn't seem to be valid JSON.\";
 meta.error = {
 message: \"JSON parse error\",
 rawBody: fetchResponse.body,
 parseError: e.toString()
 };
 result = { answer: answer, meta: meta };
 }

 if (todoItem) {
 // Step 2: Summarize the TODO item using an LLM
 meta.steps.push(\"Summarizing TODO item with LLM\");
 var prompt = \"Summarize the following TODO item in a concise sentence: \" + JSON.stringify(todoItem);
 var summarizeTask = executeTask(prompt, \"gpt-4o-mini\", \"openai\");

 if (!summarizeTask.success) {
 answer = \"While I managed to fetch your TODO details, the summary tool had a hiccup and couldn't provide me with a concise sentence right now. We seem to be experiencing some temporary issues.\";
 meta.error = {
 message: \"LLM task execution failed\",
 task_id: summarizeTask.task_id,
 error: summarizeTask.error
 };
 result = { answer: answer, meta: meta };
 } else {
 answer = summarizeTask.response;
 meta.llmResponse = summarizeTask.response;
 result = { answer: answer, meta: meta };
 }
 }
}

result;"
}
</code></pre>

</div>



A few important details here:

<ul>
<li>
It encodes error handling:

<ul>
<li>If HTTP fails → user-friendly <code>answer</code>, raw error in <code>meta.error</code>.</li>
<li>If JSON parse fails → similar pattern.</li>
<li>If the inner LLM call fails → fallback message.</li>
</ul>


</li>

<li>It delegates the actual summarization to another model via <code>executeTask</code>.</li>

<li>It returns a structured <code>result</code> with both <code>answer</code> and <code>meta</code>.</li>

</ul>

This is not the model “calling tools” directly. It’s the model writing a program that calls tools.

<h3>
 
 
 3. <code>run_js</code>: execute the code in a sandbox
</h3>

The next task is <code>run_js</code>, which is just a Contenox <code>hook</code> that calls the JS sandbox: 


<div class="highlight js-code-highlight">
<pre class="highlight json"><code>{
 "name": "js_sandbox",
 "tool_name": "execute_js",
 "args": {
 "code": "{{.generate_js.code}}"
 }
}
</code></pre>

</div>



Inside the trace you can see:

<ul>
<li>An <code>httpFetch</code> log for the JSONPlaceholder URL.</li>
<li>A response with <code>status: 200 OK</code> when things finally worked.</li>
<li>
An <code>executeTask</code> log with the summarization prompt:

<ul>
<li><code>Summarize the following TODO item in a concise sentence: {"userId":1,"id":1,"title":"delectus aut autem","completed":false}</code></li>
</ul>


</li>

</ul>

The sandbox result looked roughly like: 


<div class="highlight js-code-highlight">
<pre class="highlight json"><code>{
 "ok": true,
 "result": {
 "answer": "Task titled \"delectus aut autem\" is incomplete.",
 "meta": {
 "llmResponse": "Task titled \"delectus aut autem\" is incomplete.",
 "raw": {
 "userId": 1,
 "id": 1,
 "title": "delectus aut autem",
 "completed": false
 },
 "steps": [
 "Fetching TODO item from https://jsonplaceholder.typicode.com/todos/1",
 "Summarizing TODO item with LLM"
 ]
 }
 },
 "logs": [ ... ],
 "code": "var result; ..."
}
</code></pre>

</div>



<h3>
 
 
 4. <code>eval_js_result</code>: success or retry?
</h3>

Now comes the evaluator:

<ul>
<li>It receives a description of the JS sandbox output.</li>
<li>
The system prompt is very strict:

<ul>
<li>If <code>ok</code> is true and there is a non-empty <code>result.answer</code> → respond with <code>success</code>.</li>
<li>Otherwise → respond with <code>retry</code>.</li>
</ul>


</li>

</ul>

On the successful run, it answered: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>success
</code></pre>

</div>



So the workflow does not go into <code>repair_js</code> or <code>run_js_retry</code>. Happy path.

<h3>
 
 
 5. <code>answer</code>: extract the final user message
</h3>

The final task, <code>answer</code>, is intentionally boring:

<ul>
<li>System prompt: “You are a purely extractive post-processor. Do NOT invent content. Just surface the best existing <code>answer</code> field.”
</li>
<li>
It gets:

<ul>
<li>First run (<code>run_js</code> result).</li>
<li>Second run (<code>run_js_retry</code>), if any.</li>
</ul>


</li>

<li>

Selection rule:

<ul>
<li>Take the last non-empty <code>answer</code> you see.</li>
<li>Output it verbatim.</li>
</ul>


</li>

</ul>

In our case it found: 


<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>Task titled "delectus aut autem" is incomplete.
</code></pre>

</div>



And that’s exactly what Contenox replied in chat.




<h2>
 
 
 Why this is interesting (to me, at least)
</h2>

What I originally set out to build:

<blockquote>
A runtime for deterministic, observable GenAI workflows. 
Tasks, transitions, hooks – all explicit and replayable.
</blockquote>

What I accidentally stumbled into:

<blockquote>
A multi-model, self-orchestrating agent pattern, 
where LLMs write code that uses tools, and the runtime executes and evaluates that code.
</blockquote>

The pattern looks like this:

<ol>
<li>
Planner LLM (<code>generate_js</code>)</li>
</ol>

<ul>
<li>Reads user intent + history.</li>
<li>Emits JavaScript that calls <code>httpFetch</code>, <code>executeTask</code>, <code>executeTaskChain</code>, hooks, etc.</li>
</ul>

<ol>
<li>
Execution environment (<code>run_js</code> in Goja)</li>
</ol>

<ul>
<li>Deterministic execution of that JS.</li>
<li>Full logs of every HTTP call, every inner LLM call, every step.</li>
</ul>

<ol>
<li>
Controller LLM (<code>eval_js_result</code>)</li>
</ol>

<ul>
<li>Looks at the sandbox result.</li>
<li>Decides: is this good enough? Retry? Repair?</li>
</ul>

<ol>
<li>
Repair LLM (<code>repair_js</code>, if needed)</li>
</ol>

<ul>
<li>Gets the previous code + error output.</li>
<li>Writes a fixed version of the JS.</li>
</ul>

<ol>
<li>
Answer LLM (<code>answer</code>)</li>
</ol>

<ul>
<li>Doesn’t “reason” at all.</li>
<li>Just extracts the final <code>answer</code> text safely.</li>
</ul>

All of that is expressed as an explicit state machine in Contenox.

No hidden loops, no undocumented retries, no magic glue code inside some SDK. It’s all visible in the workflow graph and trace.




To me, that’s the exciting part:

<blockquote>
You don’t have to choose between “boring deterministic workflows” and “fancy agents”. 
You can build the agent on top of deterministic workflows. 
And everything stays **self-hosted, inspectable, and auditable if you want.
</blockquote>

يتناول المقال تجربة تم فيها السماح لنموذج ذكاء اصطناعي بكتابة كود جافا سكريبت داخل بيئة مستقلة تُدعى كونتينوكس. يتأمل الكاتب في مفهوم يتعلق باستخدام الأدوات في الذكاء الاصطناعي، مقترحًا أن النماذج يجب أن تولد كودًا لاستخدام الأدوات بدلاً من إجراء مكالمات مباشرة. تم اختبار هذا النهج من خلال تنفيذ كود جافا سكريبت الذي تم إنشاؤه داخل بيئة كونتينوكس، بهدف تحسين كفاءة سير العمل في الذكاء الاصطناعي.

El artículo discute un experimento en el que se permitió a un modelo de IA escribir código JavaScript dentro de un entorno autónomo llamado Contenox. El autor reflexiona sobre un concepto relacionado con el uso de herramientas en IA, sugiriendo que los modelos deberían generar código para utilizar herramientas en lugar de realizar llamadas directas. Este enfoque se probó ejecutando el JavaScript generado dentro del entorno Contenox, con el objetivo de mejorar la eficiencia de los flujos de trabajo de IA.

L'article traite d'une expérience où un modèle d'IA a été autorisé à écrire du code JavaScript au sein d'un environnement autonome appelé Contenox. L'auteur réfléchit à un concept concernant l'utilisation des outils en IA, suggérant que les modèles devraient générer du code pour utiliser des outils plutôt que d'effectuer des appels directs. Cette approche a été testée en exécutant le JavaScript généré dans l'environnement Contenox, visant à améliorer l'efficacité des flux de travail en IA.

The article discusses an experiment where an AI model was allowed to write JavaScript code within a self-hosted runtime called Contenox. The author reflects on a concept regarding tool usage in AI, suggesting that models should generate code to utilize tools instead of direct calls. This approach was tested by executing the generated JavaScript within the Contenox environment, aiming to enhance the efficiency of AI workflows.

I Let an LLM Write JavaScript Inside My AI Runtime. Here’s What Happened

Caterpillar Inc. was always an unlikely winner in the artificial intelligence craze. It makes the bulk of its money selling the equipment like yellow earth movers that has made it a stalwart of American industry.

تظهر شركة كاتربيلر كجهة غير متوقعة في قطاع الذكاء الاصطناعي، حيث تُعرف أساسًا بتصنيع الآلات الثقيلة مثل آلات الحفر. تاريخيًا، كانت الشركة تركز على المعدات الصناعية التقليدية، مما يجعلها أقل توافقًا مع الاتجاهات التكنولوجية المدفوعة بالذكاء الاصطناعي التي اجتذبت العديد من القطاعات الأخرى. على الرغم من الاهتمام المتزايد بالذكاء الاصطناعي، لا يزال النشاط الرئيسي لشركة كاتربيلر متجذرًا في الآلات المادية، مما قد يحد من جاذبيتها في مشهد تكنولوجي سريع التطور.

Caterpillar Inc. se ha presentado como un jugador improbable en el sector de la inteligencia artificial, siendo principalmente conocida por la fabricación de maquinaria pesada como las excavadoras. La compañía ha estado históricamente enfocada en equipos industriales tradicionales, lo que la hace menos alineada con las tendencias tecnológicas impulsadas por la IA que han cautivado a muchos otros sectores. A pesar del creciente interés en la IA, el negocio principal de Caterpillar sigue anclado en la maquinaria física, lo que podría limitar su atractivo en un paisaje tecnológico en rápida evolu…

Caterpillar Inc. se présente comme un acteur improbable dans le secteur de l'intelligence artificielle, étant principalement connu pour sa fabrication de machines lourdes telles que les pelles mécaniques. L'entreprise s'est historiquement concentrée sur l'équipement industriel traditionnel, ce qui la rend moins alignée avec les tendances technologiques axées sur l'IA qui ont captivé de nombreux autres secteurs. Malgré l'intérêt croissant pour l'IA, le cœur de métier de Caterpillar reste ancré dans la machinerie physique, ce qui pourrait limiter son attrait dans un paysage technologique en évol…

Caterpillar Inc. has emerged as an unlikely player in the artificial intelligence sector, primarily known for its manufacturing of heavy machinery such as earth movers. The company has historically focused on traditional industrial equipment, making it less aligned with the AI-driven technology trends that have captivated many other sectors. Despite the growing interest in AI, Caterpillar's core business remains rooted in physical machinery, which may limit its appeal in the rapidly evolving tech landscape.

Caterpillar’s Lone Bear Says Machinery Maker Is No AI Darling

<a href="https://www.techspot.com/news/110306-microsoft-explains-how-windows-11-become-agentic-os.html" target="_blank"><img src="https://www.techspot.com/images2/news/ts3_thumbs/2025/11/2025-11-18-ts3_thumbs-d01.jpg" width="800" height="560" style="padding: 15px 0" title="Microsoft explains how Windows 11 will become an agentic OS whether you like it or not" /></a> Windows president Pavan Davuluri recently described the future of Windows as an agentic operating system, where AI bots and large language models handle the user's commands on files and computing tasks. Critics mostly greeted the idea with scorn, cursing, and frustration over the "bug-ridden slop pile" the OS currently is.... <a href="https://www.techspot.com/news/110306-microsoft-explains-how-windows-11-become-agentic-os.html">Read Entire Article</a>

وصف رئيس شركة مايكروسوفت، بavan دافولوري، خطط ويندوز 11 للتطور إلى ما يسميه 'نظام تشغيل وكيل'. ستتضمن هذه التحويلة دمج روبوتات الذكاء الاصطناعي ونماذج اللغة الكبيرة لإدارة أوامر المستخدم ومهام الحوسبة. ومع ذلك، تم استقبال هذا الإعلان بسخرية وانتقادات من المستخدمين الذين يشعرون بالإحباط من الحالة الحالية لنظام التشغيل، الذي يصفونه بأنه مليء بالأخطاء.

El presidente de Microsoft, Pavan Davuluri, ha descrito los planes para que Windows 11 evolucione hacia lo que él llama un 'sistema operativo agente'. Esta transformación implicará la integración de bots de IA y modelos de lenguaje para gestionar los comandos y tareas informáticas del usuario. Sin embargo, el anuncio ha sido recibido con escepticismo y críticas por parte de los usuarios, que están frustrados con el estado actual del sistema operativo, que describen como plagado de errores.

Le président de Microsoft, Pavan Davuluri, a décrit les projets de Windows 11 pour évoluer vers ce qu'il appelle un 'système d'exploitation agentique'. Cette transformation impliquera l'intégration de bots IA et de modèles de langage pour gérer les commandes des utilisateurs et les tâches informatiques. Cependant, cette annonce a été accueillie avec scepticisme et critiques de la part des utilisateurs frustrés par l'état actuel du système d'exploitation, qu'ils décrivent comme rempli de bogues.

Microsoft's president, Pavan Davuluri, has outlined plans for Windows 11 to evolve into what he describes as an 'agentic operating system.' This transformation will involve the integration of AI bots and large language models to manage user commands and computing tasks. However, the announcement has been met with skepticism and criticism from users who are frustrated with the current state of the operating system, which they describe as plagued by bugs.

Microsoft explains how Windows 11 will become an agentic OS whether you like it or not

Although Black Friday is still two weeks away, you can find great Nintendo Switch and Switch 2 deals now. I've collected the best from Walmart, Best Buy, and more.

مع اقتراب يوم الجمعة السوداء بعد أسبوعين، تتوفر بالفعل عروض مبكرة على أجهزة نينتندو سويتش وسويتش 2. تقدم متاجر كبيرة مثل وول مارت وبيست باي أكثر من 20 عرضًا، مما يوفر للمستهلكين فرصة لتوفير المال على منتجات الألعاب الشهيرة قبل موسم التسوق للعطلات.

A medida que se acerca el Black Friday en dos semanas, ya están disponibles ofertas anticipadas en las consolas Nintendo Switch y Switch 2. Grandes minoristas como Walmart y Best Buy están ofreciendo más de 20 ventas, brindando a los consumidores la oportunidad de ahorrar en productos de videojuegos populares antes de la locura de compras navideñas.

À l'approche de Black Friday dans deux semaines, des offres anticipées sur les consoles Nintendo Switch et Switch 2 sont déjà disponibles. Des détaillants majeurs comme Walmart et Best Buy proposent plus de 20 ventes, offrant aux consommateurs l'occasion d'économiser sur des produits de jeu populaires avant la ruée des achats de vacances.

As Black Friday approaches in two weeks, early deals on Nintendo Switch and Switch 2 consoles are already available. Major retailers like Walmart and Best Buy are offering over 20 sales, providing consumers with an opportunity to save on popular gaming products ahead of the holiday shopping rush.

Best early Black Friday Nintendo Switch deals 2025: 20+ sales out early

<A HREF="https://substack.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.MstA6dhKs3CLxJgLSEpyVK_D4Oz9SmcjeXKNnqEDzIg?"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251118/i47.jpg"></A>
<A HREF="http://www.techmeme.com/251118/p47#a251118p47" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Alex Heath / <A HREF="https://sources.news/">Sources</A>: 
<A HREF="https://substack.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.MstA6dhKs3CLxJgLSEpyVK_D4Oz9SmcjeXKNnqEDzIg?">Q&amp;A with Demis Hassabis on Gemini 3, spending most of his research time on world models, fitting the entire Google Search index into Gemini, AI bubble, and more</A>&nbsp; &mdash;&nbsp; Demis Hassabis was noticeably relaxed when he joined our virtual call from London.&nbsp; &mdash;&nbsp; It was the day before the release of Gemini 3 &hellip;

ناقش ديميس هاسابيس، المؤسس المشارك لشركة ديب مايند، تقدم نموذج جيميني 3، أحدث نموذج للذكاء الاصطناعي من جوجل، مشددًا على قدراته في نمذجة العالم ودمج كامل فهرس بحث جوجل في النظام. تناول المخاوف بشأن فقاعة الذكاء الاصطناعي وأبرز إمكانيات النموذج في تحسين تفاعلات المستخدمين وتوفير معلومات أكثر دقة. تعكس أفكار هاسابيس التزامًا بدفع حدود تكنولوجيا الذكاء الاصطناعي ودمجها في التطبيقات اليومية.

Demis Hassabis, cofundador de DeepMind, discutió los avances de Gemini 3, el último modelo de IA de Google, enfatizando sus capacidades en modelos del mundo y la integración de todo el índice de búsqueda de Google en el sistema. Abordó las preocupaciones sobre la burbuja de la IA y destacó el potencial del modelo para mejorar las interacciones con los usuarios y proporcionar información más precisa. Las ideas de Hassabis reflejan un compromiso por llevar la tecnología de IA a nuevos límites y su integración en aplicaciones cotidianas.

Demis Hassabis, co-fondateur de DeepMind, a discuté des avancées de Gemini 3, le dernier modèle d'IA de Google, en mettant l'accent sur ses capacités en modélisation du monde et l'intégration de l'ensemble de l'index de recherche de Google dans le système. Il a abordé les préoccupations concernant la bulle de l'IA et a souligné le potentiel du modèle à améliorer les interactions avec les utilisateurs et à fournir des informations plus précises. Les réflexions de Hassabis reflètent un engagement à repousser les limites de la technologie IA et son intégration dans les applications quotidiennes.

Demis Hassabis, co-founder of DeepMind, discussed the advancements of Gemini 3, Google's latest AI model, emphasizing its capabilities in world modeling and fitting the entire Google Search index into the system. He addressed concerns about the AI bubble and highlighted the model's potential to enhance user interactions and provide more accurate information. Hassabis's insights reflect a commitment to pushing the boundaries of AI technology and its integration into everyday applications.

Q&A with Demis Hassabis on Gemini 3, spending most of his research time on world models, fitting the entire Google Search index into Gemini, AI bubble, and more (Alex Heath/Sources)

The race to deploy <a href="https://venturebeat.com/ai/6-proven-lessons-from-the-ai-projects-that-broke-before-they-scaled">agentic AI is on</a>. Across the enterprise, systems that can plan, take actions and collaborate across business applications promise unprecedented efficiency. But in the rush to automate, a critical component is being overlooked: Scalable security. We are building a workforce of digital employees without giving them a secure way to log in, access data and do their jobs without creating catastrophic risk.The fundamental problem is that traditional identity and access management (IAM) designed for humans breaks at agentic scale. Controls like static roles, long-lived passwords and one-time approvals are useless when non-human identities can outnumber human ones by 10 to one. To harness the power of agentic AI, identity must evolve from a simple login gatekeeper into the dynamic control plane for your entire AI operation.“The fastest path to responsible AI is to avoid real data. Use synthetic data to prove value, then earn the right to touch the real thing.” — Shawn Kanungo, keynote speaker and innovation strategist; bestselling author of The Bold Ones<h2>Why your human-centric IAM is a sitting duck</h2><a href="https://venturebeat.com/ai/what-could-possibly-go-wrong-if-an-enterprise-replaces-all-its-engineers">Agentic AI</a> does not just use software; it behaves like a user. It authenticates to systems, assumes roles and calls APIs. If you treat these agents as mere features of an application, you invite invisible privilege creep and untraceable actions. A single over-permissioned agent can exfiltrate data or trigger erroneous business processes at machine speed, with no one the wiser until it is too late.The static nature of legacy IAM is the core vulnerability. You cannot pre-define a fixed role for an agent whose tasks and required data access might change daily. The only way to keep access decisions accurate is to move policy enforcement from a one-time grant to a continuous, runtime evaluation.Prove value before production dataKanungo’s guidance offers a practical on-ramp. Start with synthetic or masked datasets to validate agent workflows, scopes and guardrails. Once your policies, logs and break-glass paths hold up in this sandbox, you can graduate agents to real data with confidence and clear audit evidence.Building an identity-centric operating model for AISecuring this new workforce requires a shift in mindset. <a href="https://venturebeat.com/ai/large-reasoning-models-almost-certainly-can-think">Each AI agent</a> must be treated as a first-class citizen within your identity ecosystem.First, every agent needs a unique, verifiable identity. This is not just a technical ID; it must be linked to a human owner, a specific business use case and a software bill of materials (SBOM). The era of shared service accounts is over; they are the equivalent of giving a master key to a faceless crowd.Second, replace set-and-forget roles with session-based, risk-aware permissions. Access should be granted just in time, scoped to the immediate task and the minimum necessary dataset, then automatically revoked when the job is complete. Think of it as giving an agent a key to a single room for one meeting, not the master key to the entire building.<h2>Three pillars of a scalable agent security architecture</h2>Context-aware authorization at the core. Authorization can no longer be a simple yes or no at the door. It must be a continuous conversation. Systems should evaluate context in real time. Is the agent’s digital posture attested? Is it requesting data typical for its purpose? Is this access occurring during a normal operational window? This dynamic evaluation enables both security and speed.Purpose-bound data access at the edge. The final line of defense is the data layer itself. By embedding policy enforcement directly into the data query engine, you can enforce row-level and column-level security based on the agent’s declared purpose. A customer service agent should be automatically blocked from running a query that appears designed for financial analysis. Purpose binding ensures data is used as intended, not merely accessed by an authorized identity.Tamper-evident evidence by default. In a world of autonomous actions, auditability is non-negotiable. Every access decision, data query and API call should be immutably logged, capturing the who, what, where and why. Link logs so they are tamper evident and replayable for auditors or incident responders, providing a clear narrative of every agent’s activities.<h2>A practical roadmap to get started</h2>Begin with an identity inventory. Catalog all non-human identities and service accounts. You will likely find sharing and over-provisioning. Begin issuing unique identities for each agent workload.Pilot a just-in-time access platform. Implement a tool that grants short-lived, scoped credentials for a specific project. This proves the concept and shows the operational benefits.Mandate short-lived credentials. Issue tokens that expire in minutes, not months. Seek out and remove static API keys and secrets from code and configuration.Stand up a synthetic data sandbox. Validate agent workflows, scopes, prompts and policies on synthetic or masked data first. Promote to real data only after controls, logs and egress policies pass.Conduct an agent incident tabletop drill. Practice responses to a leaked credential, a prompt injection or a tool escalation. Prove you can revoke access, rotate credentials and isolate an agent in minutes.<h2>The bottom line</h2>You cannot manage an <a href="https://venturebeat.com/ai/from-human-clicks-to-machine-intent-preparing-the-web-for-agentic-ai">agentic, AI-driven future</a> with human-era identity tools. The organizations that will win recognize identity as the central nervous system for AI operations. Make identity the control plane, move authorization to runtime, bind data access to purpose and prove value on synthetic data before touching the real thing. Do that, and you can scale to a million agents without scaling your breach risk. Michelle Buckner is a former NASA Information System Security Officer (ISSO). Read more from our <a href="https://venturebeat.com/datadecisionmakers">guest writers</a>. Or, consider submitting a post of your own! See our <a href="https://venturebeat.com/guest-posts">guidelines here</a>.

تتسارع وتيرة نشر الذكاء الاصطناعي الوكالي، مما يعد بكفاءة غير مسبوقة عبر المؤسسات. ومع ذلك، تظهر مشكلة كبيرة مع أنظمة إدارة الهوية والوصول التقليدية (IAM)، التي تفشل في تأمين هؤلاء الموظفين الرقميين. حيث يمكن أن تتجاوز الهويات غير البشرية الهويات البشرية بمعدل عشرة إلى واحد، فإن الضوابط الحالية لـ IAM، مثل الأدوار الثابتة وكلمات المرور طويلة الأمد، غير كافية. لإدارة الذكاء الاصطناعي الوكالي بفعالية، يجب أن تتطور إدارة الهوية من مجرد بوابة تسجيل دخول إلى خطة تحكم ديناميكية يمكنها التعامل مع تعقيدات عمليات الذكاء الاصطناعي.

El despliegue de la IA agentiva está acelerándose, prometiendo una eficiencia sin precedentes en las empresas. Sin embargo, surge un problema significativo con los sistemas tradicionales de gestión de identidades y accesos (IAM), que están fallando en asegurar a estos empleados digitales. A medida que las identidades no humanas pueden superar a las humanas en una proporción de diez a uno, los controles IAM existentes, como los roles estáticos y las contraseñas de larga duración, son inadecuados. Para gestionar eficazmente la IA agentiva, la gestión de identidades debe evolucionar de un simple mecanismo de inicio de sesión a un plano de control dinámico que pueda manejar las complejidades de las operaciones de IA.

Le déploiement de l'IA agentique s'accélère, promettant une efficacité accrue au sein des entreprises. Cependant, un problème majeur se pose avec les systèmes traditionnels de gestion des identités et des accès (IAM), qui échouent à sécuriser ces employés numériques. Alors que les identités non humaines peuvent dépasser les identités humaines par un facteur de dix, les contrôles IAM existants, tels que les rôles statiques et les mots de passe à long terme, sont inadéquats. Pour gérer efficacement l'IA agentique, la gestion des identités doit évoluer d'un simple mécanisme de connexion à un plan de contrôle dynamique capable de gérer les complexités des opérations d'IA.

The deployment of agentic AI is accelerating, promising enhanced efficiency across enterprises. However, a significant issue arises with traditional identity and access management (IAM) systems, which are failing to secure these digital employees. As non-human identities may outnumber human ones by a factor of ten, existing IAM controls like static roles and long-lived passwords are inadequate. To effectively manage agentic AI, identity management must transition from a simple login mechanism to a dynamic control plane that can handle the complexities of AI operations.

Human-centric IAM is failing: Agentic AI requires a new identity control plane

Groundbreaking power delivery chiplet cuts compute power needs by more than 50% to unlock the next era of AI PowerLattice, the company reimagining power delivery for next-generation AI accelerators, today announced its emergence from stealth with $25 million in Series A funding jointly led by Playground Global and Celesta Capital....
The post <a href="https://ai-techpark.com/powerlattice-raises-25-million-to-break-the-ai-power-wall/">PowerLattice Raises $25 Million to Break the AI Power Wall</a> first appeared on <a href="https://ai-techpark.com">AI-Tech Park</a>.

أعلنت شركة PowerLattice عن خروجها من مرحلة التطوير السري مع نجاحها في جمع 25 مليون دولار في جولة تمويل من الفئة A، بقيادة Playground Global وCelesta Capital. تركز الشركة على تطوير شرائح توصيل الطاقة التي تقلل بشكل كبير من احتياجات الطاقة الحاسوبية بأكثر من 50%. من المتوقع أن يسهل هذا التقدم الجيل التالي من تقنيات الذكاء الاصطناعي، مما يعالج التحديات الحرجة للطاقة التي تواجه مسرعات الذكاء الاصطناعي.

PowerLattice ha salido de su fase de desarrollo secreto con una exitosa ronda de financiación de 25 millones de dólares en la serie A, liderada por Playground Global y Celesta Capital. La empresa se centra en el desarrollo de chiplets de entrega de energía que reducen significativamente las necesidades de potencia de cálculo en más del 50%. Este avance se espera que facilite la próxima generación de tecnologías de IA, abordando los desafíos críticos de potencia que enfrentan los aceleradores de IA.

PowerLattice a émergé de sa phase de développement secret avec une levée de fonds de 25 millions de dollars lors d'un tour de financement de série A, dirigé par Playground Global et Celesta Capital. L'entreprise se concentre sur le développement de chiplets de distribution d'énergie qui réduisent considérablement les besoins en puissance de calcul de plus de 50 %. Cette avancée devrait faciliter la prochaine génération de technologies d'IA, en répondant aux défis critiques de puissance rencontrés par les accélérateurs d'IA.

PowerLattice has emerged from stealth mode with a successful $25 million Series A funding round, led by Playground Global and Celesta Capital. The company is focused on developing innovative power delivery chiplets that significantly reduce compute power requirements by over 50%. This advancement is expected to facilitate the next generation of AI technologies, addressing the critical power challenges faced by AI accelerators.

PowerLattice Raises $25 Million to Break the AI Power Wall

48 MW Incheon campus is first phase of a 500 MW AI and cloud buildout plan for Korea Princeton Digital Group (PDG), Asia Pacific&#8217;s leading data center operator, today announced its entry into South Korea, its seventh country after Singapore, Japan, India, Indonesia, China and Malaysia. PDG plans to develop multiple...
The post <a href="https://ai-techpark.com/pdg-enters-south-korea-with-usd-700-million-data-center-investment/">PDG Enters South Korea with USD 700 Million Data Center Investment</a> first appeared on <a href="https://ai-techpark.com">AI-Tech Park</a>.

أعلنت مجموعة برينستون الرقمية (PDG)، وهي مشغل رائد لمراكز البيانات في منطقة آسيا والمحيط الهادئ، عن دخولها إلى كوريا الجنوبية باستثمار كبير قدره 700 مليون دولار. يمثل هذا الاستثمار المرحلة الأولى من خطة أوسع لتطوير بنية تحتية للذكاء الاصطناعي والسحابة بقدرة 500 ميغاوات في البلاد. سيكون المشروع الأول في حرم إنشيون، الذي سيوفر قدرة 48 ميغاوات. تمثل هذه التوسعة في كوريا الجنوبية الدولة السابعة التي تعمل فيها PDG، بعد سنغافورة واليابان والهند وإندونيسيا والصين وماليزيا.

Princeton Digital Group (PDG), un operador líder de centros de datos en Asia-Pacífico, ha anunciado su entrada en Corea del Sur con una inversión significativa de 700 millones de dólares. Esta inversión marca la primera fase de un plan más amplio para desarrollar una infraestructura de IA y nube de 500 MW en el país. El proyecto inicial se ubicará en el campus de Incheon, que proporcionará 48 MW de capacidad. Esta expansión en Corea del Sur representa el séptimo país de operación de PDG, después de Singapur, Japón, India, Indonesia, China y Malasia.

Princeton Digital Group (PDG), un opérateur de centres de données de premier plan en Asie-Pacifique, a annoncé son entrée sur le marché sud-coréen avec un investissement significatif de 700 millions USD. Cet investissement marque la première phase d'un plan plus large visant à développer une infrastructure AI et cloud de 500 MW dans le pays. Le projet initial sera situé sur le campus d'Incheon, qui fournira une capacité de 48 MW. Cette expansion en Corée du Sud représente le septième pays d'opération de PDG, après Singapour, le Japon, l'Inde, l'Indonésie, la Chine et la Malaisie.

Princeton Digital Group (PDG), a leading data center operator in Asia Pacific, has announced its entry into South Korea with a significant investment of USD 700 million. This investment marks the first phase of a broader plan to develop a 500 MW AI and cloud infrastructure in the country. The initial project will be located at the Incheon campus, which will provide 48 MW of capacity. This expansion into South Korea represents PDG's seventh country of operation, following its presence in Singapore, Japan, India, Indonesia, China, and Malaysia.

PDG Enters South Korea with USD 700 Million Data Center Investment

Integration of Dell Technologies infrastructure with QuEra quantum accelerator demonstrates practical pathways to hybrid quantum–classical workflows. QuEra Computing, the leader in neutral-atom quantum computers, announced today that it is working with Dell Technologies to help define the future of high-performance computing (HPC). Using Dell solutions, QuEra is demonstrating how quantum...
The post <a href="https://ai-techpark.com/quera-to-showcase-quantum-classical-integration-at-sc25/">QuEra to Showcase Quantum/Classical Integration at SC25</a> first appeared on <a href="https://ai-techpark.com">AI-Tech Park</a>.

أعلنت شركة كويرا كومبيوتينغ، الرائدة في مجال الحواسيب الكمومية ذات الذرات المحايدة، عن تعاونها مع شركة ديل تكنولوجيز لعرض دمج الحوسبة الكمومية والتقليدية في مؤتمر SC25. يهدف هذا التعاون إلى إظهار طرق عملية لتدفقات العمل الهجينة بين الكم والتقليدي، مستفيدًا من بنية ديل التحتية إلى جانب مسرع كويرا الكمومي. من المقرر أن تحدد هذه المبادرة مستقبل الحوسبة عالية الأداء (HPC) وتبرز الأهمية المتزايدة للتكنولوجيا الكمومية في تعزيز القدرات الحاسوبية.

QuEra Computing, líder en computadoras cuánticas de átomos neutros, ha anunciado su colaboración con Dell Technologies para mostrar la integración de la computación cuántica y clásica en SC25. Esta asociación tiene como objetivo demostrar caminos prácticos para flujos de trabajo híbridos cuántico-clásicos, aprovechando la infraestructura de Dell junto con el acelerador cuántico de QuEra. La iniciativa está destinada a definir el futuro de la computación de alto rendimiento (HPC) y resalta la creciente importancia de la tecnología cuántica en la mejora de las capacidades computacionales.

QuEra Computing, leader dans le domaine des ordinateurs quantiques à atomes neutres, a annoncé sa collaboration avec Dell Technologies pour présenter l'intégration de l'informatique quantique et classique lors de SC25. Ce partenariat vise à démontrer des voies pratiques pour des flux de travail hybrides quantiques-classiques, en utilisant l'infrastructure de Dell aux côtés de l'accélérateur quantique de QuEra. Cette initiative est destinée à définir l'avenir de l'informatique haute performance (HPC) et souligne l'importance croissante de la technologie quantique dans l'amélioration des capacit…

QuEra Computing, a leader in neutral-atom quantum computers, has announced its collaboration with Dell Technologies to showcase the integration of quantum and classical computing at SC25. This partnership aims to demonstrate practical pathways for hybrid quantum-classical workflows, leveraging Dell's infrastructure alongside QuEra's quantum accelerator. The initiative is set to define the future of high-performance computing (HPC) and highlights the growing importance of quantum technology in enhancing computational capabilities.

Keyfactor Validates PKI-Based Identity for Securing Agentic AI

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