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- A new open-source AI agent moves from text to real action.
- Local control brings power but raises security risks.
- Agentic engineering signals a shift in how software gets built.
According to the interview, the developer has described a new way to build AI. He calls it agentic engineering. The idea sounds simple. The agent understands its own code, setup, and models. It can inspect itself. It can change itself.
The concept grew from long nights of experimental coding and quiet regret. That developer is Peter Steinberger. He spent more than a decade building PSPDFKit. After stepping away from daily coding, he returned with fresh curiosity.
A one-hour prototype sparked a larger vision. That prototype later became OpenClaw. The project spread fast. Developers shared it across GitHub. Stars and forks climbed at record speed. Many called it the biggest AI moment in years. The reason felt clear. This agent did not just talk. It acted.
The AI Agent Handles Tasks Across the System
It works with local devices. It connects to messaging apps. AI works with multiple AI models. It can be talked to in the same way a person can be talked to. AI then completes the tasks through the system.
The level of access is what makes it unique. The agent has the whole view of its surroundings. It is aware of the way tools integrate. This makes it extremely powerful. It can also cause harm if it makes a mistake. Steinberger has warned people about this.
Being security-conscious is just as important as being creative. The process is similar to the larger trend. The world of AI is no longer outside the prompt. It is now inside the workflow. This is what OpenClaw does. It takes ideas and makes them happen. The open-source community is helping to improve it. They are adding new features to it.
Some of the people working on it have not even coded before. The barrier is getting lower and lower. The discussion of the trend has reached a lot of people. Steinberger was a guest on the Lex Fridman Podcast. He talked about an elementary concept. Agents work when they are made to feel.
Steinberger Builds Smarter Assistant
Before that, Steinberger tried a personal assistant that scanned chat history and images. It also utilized early large context models from OpenAI. They were emotional and useful. Steinberger believed that large teams would soon finish it.
They did not. Steinberger got frustrated. So, he decided to create his own agent. Steinberger’s breakthrough came when his agent could solve things without direct instruction. It could convert files. It could also choose tools. Steinberger even figured out voice messages without direct instruction at some point.
This was a breakthrough moment for Steinberger. His agent understood the world. It also understood itself. Steinberger saw a new world beginning. Software does not just wait for direct instructions anymore. It also helps.
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