Not long ago, the idea of having a personal digital workforce was reserved for science fiction or corporate budgets large enough to support specialized automation teams. Most people simply assumed that complex task automation would remain out of reach for everyday users. Even as AI systems became more available, the promise of hands-off productivity remained theoretical. You could prompt a model to explain something. You could ask it to brainstorm. But building an actual repeatable workflow required plugins, APIs, and technical fluency that most people do not possess.
Kortix’s founders, Domenico Gagliardi and Marko Kraemer, believed that gap was holding back the real value of consumer AI. After introducing “Starters”, a set of focused tools that outperform traditional AI systems on presentations, data, and research tasks, Gagliardi turned to a more ambitious question. If AI can execute tasks, why can it not do them continuously, on schedule, and across different platforms without any manual prompting?
The answer materialized in a feature called “Workers”. In the landscape of consumer AI, it may be one of the most significant steps toward true personal automation.
From Single Tasks to an AI Workforce
“Starters” were designed to help users complete discrete tasks. Workers take the next step. Instead of asking an AI to create a report or summarize a document, users can create an agent that performs these tasks every week without being asked, connected directly to the tools where the work happens.
“Workers” are built with natural language. There is no code, no installation process, no complex configuration. A user simply describes what they need. The platform interprets the instruction and builds an autonomous agent capable of executing the workflow.
The experience feels less like programming and more like managing a team. A “Worker” can be told, “You handle my Google Calendar,” or “You are responsible for gathering market news and sending me a summary each morning.” Kortix then runs the automation on schedule, pulling data, generating insights, and completing tasks across Slack, Google Drive, Gmail, and other connected platforms.
In a world where AI often feels abstract, “Workers” make the concept tangible.
The Power of Scheduled, Cross-Platform AI
What makes “Workers” remarkable is not just their autonomy, but their ability to operate across different environments. AI becomes significantly more useful when it can move through the same digital spaces a human would. “Workers” can read files from Drive, send emails through Gmail, post on Slack, gather information from the web, and compile everything into usable outputs.
This means a “Worker” can be responsible for a full workflow rather than a single action. For example, a marketing professional can create an agent that scrapes industry trends every Friday morning, prepares a set of LinkedIn post drafts, and uploads them for review. A founder can build a “Worker” that scans competitor updates, organizes notes into a shared folder, and alerts the team when meaningful changes appear online.
Instead of jumping between tools or manually copying results from one platform to another, users receive a consistent output delivered exactly where they need it.
“People waste an enormous amount of time switching between systems,” Gagliardi says. “‘Workers’ eliminate that friction. You explain what you want done, and the platform handles everything in sequence.”
A More Accessible Vision of Automation
Most automation tools still assume a baseline level of technical expertise. Kortix takes a different approach. The entire feature is built on the belief that users should not need to understand programming in order to orchestrate complex workflows.
This is where the philosophy behind Kortix becomes clear. The same open source foundation that gives “Starters” their precision enables “Workers” to act with flexibility. Because everything is transparent and customizable, the platform can parse instructions in plain language and translate them into automated routines.
If someone wants to build a “Worker” that organizes their inbox, they describe the expectation. If they want a “Worker” that rewrites weekly reports and stores them in a shared drive, they type the request like they would to a colleague.
This creates a new kind of relationship with AI. The user is not prompting. They are delegating.
Why Personal Automation Matters Now
The modern digital environment asks individuals to manage far more information than any person can reasonably process. Teams are fragmented across tools. Notifications appear from every direction. Workflows are increasingly nonlinear, requiring constant context switching.
“Workers” enter that landscape with a stabilizing function. They create predictable rhythms inside unpredictable days.
As Gagliardi explains it, “Most people do not need more tools. They need continuity. They need the feeling that something reliable is always working in the background.”
This sentiment reflects a major shift in how people view AI. The first wave of excitement centered on novelty. The next wave focuses on utility. “Workers” belong entirely to this second category.
When AI Stops Feeling Like Software
As users experiment with Kortix’s “Workers”, many describe a similar reaction. The transition from single task AI to autonomous agent AI changes the way people think about their digital life. The boundary between “tools I use” and “help I receive” begins to dissolve.
A “Worker” will show up on schedule even if the user forgets that it exists. It will handle inbox triage while the user sleeps. It will send reminders, create content, track information, and maintain routines that usually require attention and mental energy.
Autonomy gives AI a new dimension. It stops feeling like software and starts feeling like support.
This is why “Workers” are positioned as the natural evolution of Starters. Where “Starters” solve individual problems, “Workers: solve ongoing ones. They turn AI into infrastructure rather than assistance.
Building Toward a New Standard in Consumer AI
Kortix’s early adopters are already using “Workers” to run functions that normally require multiple tools or multiple people. The creator’s batch continues to test how far this system can go, offering the kind of real world feedback that has shaped the platform from the beginning.
The significance of “Workers” becomes clearer when placed within the broader trajectory of AI. As general models become more capable, their usefulness will depend on context and continuity. The platforms that succeed will be the ones that turn raw intelligence into coordinated action.
Gagliardi sees “Worker”s as a step in that direction. “AI should work alongside you, not just answer you,” he says. “If we give people the ability to build their own workforce, they will get more out of AI than they ever imagined.”
In a market overflowing with large language models and novelty tools, Kortix is quietly building something different. It is constructing a pathway toward personal automation that does not require technical expertise, large budgets, or enterprise level resources.
For millions of people drowning in repetitive digital work, that possibility feels extraordinary.
