automation

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Why AI agents need interaction infrastructure

To stop automation waste, enterprises must deploy interaction infrastructure that physically governs how independent AI agents operate. AI agents now populate corporate networks, reasoning through tasks and executing decisions with increasing autonomy. Yet, when these independent actors attempt to coordinate work, exchange context, or operate across varied cloud environments, the interaction framework degrades quickly. Human […]

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Siemens introduces AI system for automation engineering

Siemens has introduced the Eigen Engineering Agent, an AI system designed to plan and validate automation engineering tasks in operational environments. The system uses multi-step reasoning and self-correction to carry out tasks autonomously and operates directly inside engineering platforms, letting it to complete workflows from initial design through to validation. Autonomous engineering workflows The agent

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Microsoft open-source toolkit secures AI agents at runtime

A new open-source toolkit from Microsoft focuses on runtime security to force strict governance onto enterprise AI agents. The release tackles a growing anxiety: autonomous language models are now executing code and hitting corporate networks way faster than traditional policy controls can keep up. AI integration used to mean conversational interfaces and advisory copilots. Those

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KiloClaw targets shadow AI with autonomous agent governance

With the launch of KiloClaw, enterprises now have a tool to enforce governance over autonomous agents and manage shadow AI. While businesses spent the last year securing large language models and formalising vendor agreements, developers and knowledge workers started moving on their own. Employees are bypassing official procurement, deploying autonomous agents on personal infrastructure to

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Autonomous AI systems depend on data governance

Much of the current focus on AI safety has centred on models – how they are trained and monitored. But as systems become more autonomous, attention is changing toward the data those systems depend on. If the data feeding an AI system is fragmented, outdated, or lacks oversight, the system’s behaviour can become more unpredictable.

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Hershey applies AI across its supply chain operations

Artificial intelligence is moving beyond software and further into the physical side of business. Companies in food production and logistics are starting to use data systems to support day-to-day decisions, not long-term planning. That change is visible in The Hershey Company’s latest strategy update. At its Investor Day, the company said it plans to use

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SAP and ANYbotics drive industrial adoption of physical AI

Heavy industry relies on people to inspect hazardous, dirty facilities. It’s expensive, and putting humans in these zones carries obvious safety risks. Swiss robot maker ANYbotics and software company SAP are trying to change that. ANYbotics’ four-legged autonomous robots will be connected straight into SAP’s backend enterprise resource planning software. Instead of treating a robot

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RPA matters, but AI changes how automation works

RPA (robotic process automation) is a practical and proven way to reduce manual work in business processes without AI systems. By using software bots to follow fixed rules, companies can automate repetitive tasks like data entry and invoice processing, and to a certain extent, report generation. Adoption grew quickly in many sectors, especially in finance,

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Automating complex finance workflows with multimodal AI

Finance leaders are automating their complex workflows by actively adopting powerful new multimodal AI frameworks. Extracting text from unstructured documents presents a frequent headache for developers. Historically, standard optical character recognition systems failed to accurately digitise complex layouts, frequently converting multi-column files, pictures, and layered datasets into an unreadable mess of plain text. The varied

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IntelliLoad: Building a Smarter AI-Assisted Load Testing Tool

While everyone around me was busy exploring the latest AI tools, I decided to take a slightly different path — exploring AI for load testing. I tried popular tools like K6, TestSprite, and JMeter, learning how they simulate traffic and monitor app performance. But soon I realized: why settle for existing tools when I could

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