Learn more about Loop Engineering here → https://ibm.biz/~LqlzZo42O

AI agents need more than one perfectly written prompt. Martin Keen explains loop engineering and how AI agents act, observe, adjust, and repeat toward measurable goals. Learn how context, tools, subagents, and human oversight make agentic loops work.

00:00 – Understand Loop Engineering vs Prompt Engineering
01:18 – Build an AI Agent Loop Around a Goal
02:30 – Define Goals AI Agents Can Actually Verify
03:29 – Engineer Triggers, Schedules & Hooks
04:18 – Manage Agent Context, Skills & Persistent State
05:37 – Give AI Agents Tools with MCP & APIs
06:10 – Use Subagents to Check AI Agent Work
07:11 – Keep Humans Inside Agentic Loops
07:49 – Avoid Comprehension & Intent Debt
10:05 – Define the Human Role in Loop Engineering

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/~FCnbZpRqy

AI was used in the creation of the transcript and metadata for this video.

#loopengineering #aiagents #agenticai #promptengineering #contextengineering
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DeepSeek V4.1 Flash makes motion design that sits right next to Claude Opus 5.5. It’s faster, cheaper, open source, and free inside OpenCode. See the side-by-side tests, plus the Open Design Motion setup that lets you build explainers, demos, and edits in minutes.

00:00 Intro – DeepSeek V4.1 Flash motion design
00:51 Examples – Explainers, demos, vertical + landscape
02:06 DeepSeek vs Opus 5.5 – Side-by-side
03:06 Open Design Motion – Setup + templates
04:04 Auto-Posting – Script, edit, post, analytics
05:28 Real Results – 40K-view post
06:22 Free Setup – OpenCode + Hermes + Open Design

Learn more about Loop Engineering here → https://ibm.biz/~LqlzZo42O

AI agents need more than one perfectly written prompt. Martin Keen explains loop engineering and how AI agents act, observe, adjust, and repeat toward measurable goals. Learn how context, tools, subagents, and human oversight make agentic loops work.

00:00 – Understand Loop Engineering vs Prompt Engineering
01:18 – Build an AI Agent Loop Around a Goal
02:30 – Define Goals AI Agents Can Actually Verify
03:29 – Engineer Triggers, Schedules & Hooks
04:18 – Manage Agent Context, Skills & Persistent State
05:37 – Give AI Agents Tools with MCP & APIs
06:10 – Use Subagents to Check AI Agent Work
07:11 – Keep Humans Inside Agentic Loops
07:49 – Avoid Comprehension & Intent Debt
10:05 – Define the Human Role in Loop Engineering

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/~FCnbZpRqy

AI was used in the creation of the transcript and metadata for this video.

#loopengineering #aiagents #agenticai #promptengineering #contextengineering
---------------------------------------------------------------------------------------------------------
Find us on YouTube:
🔵 IBM Technology: https://www.youtube.com/@IBMTechnology
🔵 IBM: https://www.youtube.com/@IBM
🔵 IBM Developer: https://youtube.com/@IBMDeveloperAdvocates

Ein Abend für herausragende IT-Lösungen und die Unternehmen dahinter: Bei der Gala der IT-Awards 2026 in Augsburg wurden die Sieger der Leserwahl ausgezeichnet. In diesem Video zeigen wir die Highlights des Galaabends – von der Preisverleihung bis zu den Begegnungen und Eindrücken vor Ort. Die Awards würdigen Anbieter, die sich in der großen Leserwahl der Insider-Portale der Vogel IT-Medien GmbH durchsetzen konnten.

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NVIDIA’s new open model, Nemotron 3.5 Super VL, finishes coding tasks up to 4x faster, reads screenshots, charts and PDFs, and beats NVIDIA’s own bigger Ultra model on agent benchmarks. Here’s what it does, how to use it, and the one catch you should know before you try it.

00:00 Intro – An open model that codes 4x faster
00:40 What Is Nemotron – NVIDIA’s open AI models for agents
01:31 Super VL Launch – Text, image, document and video in one model
01:46 How It Stays Fast – 120B parameters, only 12B active
02:31 4x Faster Coding – Speed and accuracy on SWE-Bench Pro
03:48 Beats Ultra – Smaller model outperforms NVIDIA’s flagship
04:22 Custom Training – 41% accuracy gain for cybersecurity
05:24 Document AI – Reading charts, tables and text in images
06:35 Where to Get It – Hugging Face, OpenRouter, LM Studio
07:10 The Catch – Training data isn’t public yet

NEURA Robotics just unveiled Neuron AI, a perception-language-action foundation model for physical AI that adds touch, sound, memory, and 3D vision to robot control. NEURA says it runs locally on the robot and trains on real-world, simulation, and human demonstration data. Then, inside Boston Dynamics’ prototype operations team, an in-house machine shop is cutting robot build time from about a day toward an hour and then minutes, according to the team. DeepSeek V4.1 Flash 1017 scores 77% on DeepSWE at under $0.20 per task, based on DeepSeek’s own benchmarks. Google shows Gemini 4 ARGON in Canvas turning dense PDFs into interactive, animated web apps you can explore at your own pace, and rendering a 3D room in vanilla Python. Finally, a robot learns soccer through self-play against earlier versions of itself. Independent results are still pending, so every company claim here is clearly attributed. Subscribe for the latest AI and robotics news.

Discover the 8004 AI agent economy: https://8004agents.ai

AI News:

0:00 NEURA Neuron AI
0:38 Four Senses
1:28 No Cloud Needed
1:50 How It Learns
2:16 Boston Dynamics Prototype Shop
3:50 Robot Build Time: A Day to Minutes
4:59 DeepSeek V4.1 Flash Cuts Cost Per Task
5:50 Gemini 4 ARGON Turns PDFs Into Web Apps
6:17 Gemini 4 ARGON 3D Room in Python
7:01 Robot Learns Soccer by Self-Play

#AINews #Robotics #HumanoidRobots #NEURA #BostonDynamics #DeepSeek #Gemini