AI agents are systems that do not merely answer but act: they call tools, work through tasks and make their own intermediate decisions along the way. This overview brings together the fundamentals, the fields of use in companies, the state of development and the risks that come with it.
Start here: From Text Generator to Digital Employee explains in four stages how language models turned into acting systems. Paperclip: When AI Agents Get an Org Chart shows in a real case what several agents together deliver and cost. Why Robust Governance Is Indispensable takes the control question apart.
If you want to build it yourself, the hands-on side lives in the two tool overviews: OpenClaw for a single agent on your own hardware and n8n for automated workflows.
OpenAI is working on GPT-6 as an autonomous agent for companies. At the same time, Moonshot AI is presenting Kimi K3, an open model that can be run locally and could shift the competitive landscape in the AI sector.
With the Agents API, OpenAI provides developers with an interface for autonomous cloud agents. The API is in public beta and makes it possible to build agents that carry out tasks independently, process code, and use subagents.
Google Research has developed a framework that provides AI agents with a permanent knowledge base in wiki form. Failures and successes are stored there, so that the agents can keep learning.
With GPT-6 Astra, OpenAI has presented a model that is to take on complex tasks such as software development, data processing, and project management as an automated engineer. According to OpenAI, use costs less than six US dollars per hour.
Autonomous AI agents can automate complex processes, but without robust governance they risk data breaches and compliance violations. Clear roles, least-privilege access, and logging are essential.
Notice: This article was created with AI. What’s It About? At Sapphire 2026, SAP presented its vision of the “Autonomous Enterprise” — companies that automate their business processes with the help of autonomous AI agents. Several major customers showcased their concrete implementations, revealing that the maturity of AI integration varies considerably: while some corporations are
Lena’s Fourteen Tabs It’s a Tuesday morning in spring, and Lena is staring at an email from Anthropic. Subject: “Your usage limit has been reached.” €847 in 72 hours. Lena, 34, runs a small content agency in Zurich. Three employees, a dozen clients, and for the past few months, an experiment: AI agents for routine
Autonomous software programs use large language models to carry out tasks on their own. Unlike conventional chatbots they act proactively, make their own decisions and adapt to new situations.
Notice: This article was created with AI. What’s it about? Companies face the challenge of integrating AI agents safely and effectively into their production environments. A systematic checklist of criteria is intended to help address critical aspects such as data quality, security, and legal requirements before going live. Background & Context Deploying autonomous AI systems
It is a Wednesday evening in March 2023, and Bernd has a problem. In three days he is flying to Lisbon — team building, five colleagues, everything still unorganized. So he opens ChatGPT, the tool that has been electrifying everyone in his company for weeks, and types: “Plan me a three-day team trip to Lisbon.