Interrupt 2026 is back May 13–14 in San Francisco.
This year's question: how do you make agents work at enterprise scale? Here's a preview of what's on the agenda:
→ Apple on building a low-code agent platform for 15K+ employees
→ Lyft on building evals that actually tell
AUTOMATION
-
Enterprise AI Agents at Scale: Interrupt 2026 Conference Preview
By
–
-
Different UIs for AI Agents Across Developer Workstreams
By
–
i think there will probably be different UIs for different workstreams we're mostly still seeing agents for developers, so makes sense UIs are focused there unclear if UI blocks other adoption, or its just the quality of agents
-
US Team Develops Framework for Safer Robot and Autonomous Vehicle Networks
By
–
US-led team introduces framework to make networks of #Robots, self-driving cars safer
by Prabhat Ranjan Mishra @IntEngineering Learn more: https://
bit.ly/4vefwex #Robotics #EmergingTech #Technology #Innovation -
Claude Mythos and Cowork: Future of AI Software with Felix Rieseberg
By
–
Claude Cowork, Mythos, and the Future of Software: my conversation with @felixrieseberg, who leads Cowork at @AnthropicAI
— Matt Turck (@mattturck) 10 avril 2026
00:00 Intro
01:53 Claude Mythos Preview and the “step-function change”
06:16 Why Anthropic is treating Mythos differently
11:19 The real story behind… pic.twitter.com/S8ZUKSuYA3Claude Cowork, Mythos, and the Future of Software: my conversation with @felixrieseberg, who leads Cowork at @AnthropicAI 00:00 Intro 01:53 Claude Mythos Preview and the “step-function change” 06:16 Why Anthropic is treating Mythos differently 11:19 The real story behind Claude Cowork’s “10-day” build 12:42 Why Anthropic realized Claude Code needed a non-technical version 15:44 What Claude Cowork actually is 17:03 Under the hood: virtual machines, tools, skills 18:36 Where Cowork’s memory actually lives 19:26 How Cowork connects to files, apps, and the internet 20:45 Why Felix thinks the local computer is under-appreciated 24:49 Trust: how do you get users comfortable with AI agents? 28:45 What UX actually means for AI agents 31:27 Anthropic Cowork's roadmap is only one month long 34:12 Building 100 prototypes 35:10 If execution is free, what becomes the bottleneck? 37:25 Does it come down to taste? 40:12 The hardest part of building Claude Cowork 41:43 Advice for founders building AI agents 44:21 SaaSpocalypse: what’s left for software startups 49:30 Where AI agents are going next 51:20 Regulated industries and enterprise adoption 54:15 Hot takes: what's underrated, overrated, and what Felix would build today
→ View original post on X — @scobleizer, 2026-04-10 16:19 UTC
-
PetClaw: AI Desktop Pet Simplifies Agent Setup and Workflow
By
–
The classic desktop pet idea has now been turned into a full AI employee.
— Rohan Paul (@rohanpaul_ai) 10 avril 2026
This is the kind of AI product that makes browser tabs feel like the old interface.
If you’ve ever looked at OpenClaw and thought “this is cool, but I do not want setup to become the project,” @PetClaw_ai… pic.twitter.com/eIuRylxiZWThe classic desktop pet idea has now been turned into a full AI employee. This is the kind of AI product that makes browser tabs feel like the old interface. If you’ve ever looked at OpenClaw and thought “this is cool, but I do not want setup to become the project,” @PetClaw_ai is probably the first version to achieve that. PetClaw feels like someone took the agent idea and removed half the friction. It is a local AI desktop pet built on OpenClaw that installs in 1 click, stays on your desktop, works through voice and chat, remembers context, and helps with research, planning, content, and repetitive work without turning setup into the main job. What sold me first was the setup. No API key hunt, no config mess, no “why is this broken again” loop. Install it, open it, start using it. I also like that it feels more controlled than a lot of agent demos. It runs locally by default, keeps your data on your machine unless you allow otherwise, and feels a lot less reckless than giving a random agent broad access to everything. 🧵 1. Prompt: “Create a reusable research workflow for me called ‘Agent Watch’. Every time I run it, it should find recent papers, blog posts, and launches about browser agents, summarize the important changes, rank them by technical significance, and output a clean briefing with links, a comparison table, and 3 ideas worth exploring next.”
→ View original post on X — @scobleizer, 2026-04-10 16:19 UTC
-
Streamlining Design Change Tracking Through Workflow Optimization
By
–
The old workflow made tracking and implementing design changes difficult through the convoluted multi-system process.
-
NC Software Upgrade Enables High-Speed 5-Axis Machine Operations
By
–
JK Machining's previous NC programming software couldn't handle their new high-speed 5-axis machines properly.
-
Siemens NX Integrated CAD CAM Digital Thread Manufacturing
By
–
Partner content with Siemens. Old way: Design in one system. Export neutral files. Import to CAM. Fight translation errors. Struggle with design changes.
NX way: One integrated system. Digital thread. Automated everything. #sie_HM #HM26 -
OutSystems ASE: Managing Enterprise AI Risk with 363% ROI
By
–
🚨 Stop letting rogue AI break your enterprise systems.
— Charly Wargnier (@DataChaz) 10 avril 2026
At the @OutSystems launch last week, @woodson_martin proved you no longer have to choose.
Here's a hard fact:
60% of devs use 3+ AI tools, but 67% of leaders are unequipped to manage the risk.
The ultimate unfair… pic.twitter.com/0nZ0SJ3oWb🚨 Stop letting rogue AI break your enterprise systems. At the @OutSystems launch last week, @woodson_martin proved you no longer have to choose. Here's a hard fact: 60% of devs use 3+ AI tools, but 67% of leaders are unequipped to manage the risk. The ultimate unfair advantage is Outsystems' new Agentic Systems Engineering, or `ASE`. By running an AI Mentor through an Enterprise Context Graph, ASE ensures every single change respects your specific data, logic, and security rules. But don't just take my word for it. @Forrester ran the numbers and the impact is pretty massive: A staggering 363% ROI over 3 years, radically faster delivery, and millions saved on legacy updates. If you want to build at this speed, you need to try this. and ICYMI, watch the March 31st conference replay now (link in 🧵↓)
-
Building AI Agents: Knowledge, Tools, and Process Essentials
By
–
great question! knowledge (system prompt, skills) and tools (apis, clis, whatnot) will always be needed, even as the best way to build agents changes i would focus on those and processes around those