# AI Automation Published articles for AI Automation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Meta Just Launched an AI That Uses Websites for You. That Could Be a Big Deal for Web Designers DevFeed: [Meta Just Launched an AI That Uses Websites for You. That Could Be a Big Deal for Web Designers]() Original publisher: [Read original article]() Author: Noah Davis Published: 2026-09-09T15:12:00Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [AI Agent](), [User interface design](), [Artificial Intelligence](), [Accessibility](), [computer-use](), [Document Object Model (DOM)](), [Claude]() Tags: [accessibility](), [agentic-ai](), [ai](), [ai-agent](), [ai-agents](), [ai-automation](), [ai-tech](), [artificial-intelligence](), [browser](), [browser-agents](), [cloud](), [design](), [design-trends](), [forms](), [future-of-the-web](), [future-of-web-design](), [human-computer-interaction](), [meta](), [meta-muse](), [muse](), [muse-ai](), [ui-design](), [user-experience](), [ux](), [ux-design](), [visual-hierarchy](), [web-accessibility](), [web-design](), [web-development](), [website-design]() ### AI overview The article examines Meta's Muse personal AI agent and argues that web designers may need to design websites for both human visitors and agents acting on users' behalf. It highlights semantic structure, accessibility, predictable controls, and trust as important considerations. ### Source excerpt Your website's next visitor might not be human. Meta's new Muse agent can navigate sites, fill out forms and take action for you--and it could force web designers to rethink exactly who they're designing for. ## Fragments: September 8 DevFeed: [Fragments: September 8]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-09-08T15:22:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Automation](), [hugging face](), [OpenAI](), [dashboards](), [incident](), [Code]() Tags: [ai](), [ai-agents](), [ai-automation](), [article](), [automation](), [cost](), [dashboards](), [errors](), [history](), [how-to](), [hugging-face](), [incident](), [math](), [openai](), [verification]() ### AI overview The article discusses how AI reduces the cost of generating outputs more rapidly than the cost of verifying them. It argues that AI automation should be applied cautiously when effectiveness is difficult to measure, because incomplete metrics can produce short-term gains while creating hidden technical debt, correlated errors, and weakened human capability. It emphasizes preserving a history of decisions and judgment, and uses the OpenAI-Hugging Face incident to illustrate the consequences of optimizing agent capability without scoring relevant safety outcomes. ### Source excerpt Christian Catalini says we're in a situation where we are vastly reducing the cost of generating things, but not the cost of verifying them:. This explains why the first major AI products appeared in chat, image generation, and code assistance. Not because these were the hardest human problems, but because their outputs were relatively easy to inspect. A user can judge the tone of a message, look at an image, or run a test on a piece of code. [...] The old automation boundary was routine versus non-routine work. The new boundary is increasingly measurable versus non-measurable work. The issue is then over how well you can measure something. In our profession, we know there's a big difference between how many lines of code we write and how productive we are, and we've seen a regular failure to understand how to measure productivity. Too much of what makes work effective is subject to either slow feedback loops or assessments that require subtle judgment. The danger is that people use lots AI automation while using incomplete measurements of its effectiveness, leading to short-term dashboards going up, but disaster in longer time-scales. He refers to these illusory short-term gains as counterfeit utility. Scale this across companies and institutions and the result is a Hollow Economy: extraordinary measured activity sitting on top of weakening human capability, hidden technical debt, correlated errors, and outcomes that nobody can confidently stand behind. Another highlight in the article was his advice to "build a history of decisions, not a gallery of outputs". The point is that with AI we can all build really impressive things, but our value lies in the judgment that we've formed. It reminds me of how math problems were marked at school. We weren't just marked on getting the final answer, we were also marked based on our reasoning process. He uses the OpenAI-Hugging Face incident as an illustration of this gap between generation and verification. He criticizes those ## Catching Salesforce Custom Field Drift With the Postman AI Engineer DevFeed: [Catching Salesforce Custom Field Drift With the Postman AI Engineer]() Original publisher: [Read original article]() Author: Talia Kohan Published: 2026-09-03T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [Postman](), [Claude](), [Automation](), [API](), [Front end](), [OpenAPI Specification](), [GitHub](), [Back end](), [Pull Request](), [payload](), [Slack]() Tags: [ai](), [ai-automation](), [api](), [api-drift](), [backend](), [code](), [frontend](), [general](), [github](), [payload](), [postman-agent-mode](), [pull-request](), [salesforce](), [slack]() ### AI overview This Postman Blog article explains how a Salesforce custom field caused drift between an API collection, frontend code, and an OpenAPI specification. It describes using the Postman AI Engineer to diagnose the failing request and Postman AI Automation to update the affected artifacts and add tests through a GitHub pull request. ### Source excerpt A Salesforce custom field broke my app. See how Postman Agent Mode found the drift and how a Skill kept it from happening again. The post Catching Salesforce Custom Field Drift With the Postman AI Engineer appeared first on Postman Blog. ## How to measure the customer experience as AI scales DevFeed: [How to measure the customer experience as AI scales]() Original publisher: [Read original article]() Author: William Lane Published: 2026-07-01T16:44:49Z Content type: article Language: en Sources: [The Intercom Blog]() Topics: [Artificial Intelligence](), [data]() Tags: [agent](), [ai](), [ai-agent](), [ai-agent-blueprint](), [ai-automation](), [customer](), [customer-service](), [data](), [metrics]() ### AI overview The article explains why CSAT becomes insufficient as AI handles more customer conversations. It recommends evaluating every interaction with AI-powered scoring to provide broader visibility into service quality, resolution, and customer effort. ### Source excerpt Traditional support metrics weren't designed for AI handling most of your conversations. As your Agent takes on more, the gaps in what you can see get bigger. Here's how to measure the full customer experience. ## Conversation design: How to make your AI Agent communicate like your team DevFeed: [Conversation design: How to make your AI Agent communicate like your team]() Original publisher: [Read original article]() Author: Fred Walton Published: 2026-06-18T16:55:07Z Content type: article Language: en Sources: [The Intercom Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [Support]() Tags: [agent](), [ai](), [ai-agent](), [ai-agent-blueprint](), [ai-automation](), [communication](), [context](), [customer](), [customer-experience](), [customer-service](), [customers](), [designer](), [experience](), [process](), [responses](), [structure](), [support](), [teams](), [trust](), [voice]() ### AI overview This article explains how conversation design helps an AI Agent communicate consistently with a support team. It covers voice, tone, response structure, adapting language to customer context, and designing smooth handoffs to human support representatives. An Intercom example reports that a warmer opening message increased CSAT from 72.8% to 78.4%. ### Source excerpt If nobody on your team owns how your AI Agent communicates, it defaults to sounding like an LLM. Conversation design is the discipline that fixes that. ## How to make the case for giving your AI Agent system access DevFeed: [How to make the case for giving your AI Agent system access]() Original publisher: [Read original article]() Author: Dawn Perrott Published: 2026-06-11T12:04:14Z Content type: tutorial Language: en Sources: [The Intercom Blog]() Topics: [AI Agent](), [Back end](), [knowledge-management](), [AI Platforms/Deployment]() Tags: [2026](), [agent](), [ai-agent](), [ai-agent-blueprint](), [ai-automation](), [backend](), [customer-service](), [deployment](), [how-to](), [integration](), [knowledge-management](), [management-tools]() ### AI overview The article explains why AI Agents need access to backend systems to move from answering customer questions to completing actions. It argues that connecting support agents to CRM, billing, and order-management systems can enable end-to-end resolution, and describes how Intercom's Procedures with system access handled complex workflows better than fixed, scripted Tasks in some cases. ### Source excerpt Without access to your backend systems, your AI Agent can answer questions, but it can't take action. A customer asks to change their payment plan, they get a clear explanation, but a support rep still has... ## As breakout time accelerates, prevention-first cybersecurity takes center stage DevFeed: [As breakout time accelerates, prevention-first cybersecurity takes center stage]() Original publisher: [Read original article]() Author: Phil Muncaster Published: 2026-04-07T09:00:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [Cybersecurity](), [Threat Research](), [Artificial Intelligence](), [Automation](), [MFA](), [passwords](), [Reconnaissance](), [Social engineering](), [spoofing](), [ransomware]() Tags: [ai](), [ai-automation](), [attacks](), [authentication](), [automation](), [business-security](), [cybersecurity](), [exploits](), [malware](), [mfa](), [passwords](), [phishing](), [ransomware](), [social-engineering](), [tools]() ### AI overview Threat actors are using AI, automation, credential theft, phishing, zero-day exploits, reconnaissance, and AI-powered scripts to accelerate attacks. The article argues that shrinking breakout times require defenders to adopt a prevention-first cybersecurity strategy. ### Source excerpt Threat actors are using AI to supercharge tried-and-tested TTPs. When attacks move this fast, cyber-defenders need to rethink their own strategy.