Persistent Agents

Most AI programs work like a vending machine: you ask a question, they answer, then they forget everything. Persistent agents are built differently. An agent is a program that independently plans and executes steps to achieve a goal. Persistent means that this agent keeps running over a long period of time and remembers earlier steps. So it can start a task on Monday and keep working on it on Friday. In between, it observes whether anything has changed and reacts to it on its own.

From question-answer machine to temporary employee

The difference sounds technical, but it changes what AI can actually be used for. A chat program can explain to you how to plan a trip. A persistent agent can actually manage the trip over weeks. It checks prices daily, remembers your budget, and lets you know when a flight gets cheaper. Tasks consisting of many small steps over a long period of time thereby become automatable.

For businesses, this is exactly the economically interesting point. Many workflows don’t take seconds, but days: processing an application, following up on a complaint, narrowing down a software problem. Until now, a human had to hold the thread between the individual steps. Persistent agents are meant to take over that thread. That’s why companies like OpenAI, Google, and Anthropic are currently investing heavily in this direction.

But there is a downside. A program that acts unsupervised for days can also make mistakes unsupervised for days. If a chatbot writes nonsense once, you notice immediately. If an agent makes a wrong intermediate step, it builds further steps on top of it. The error grows along with it.

Memory, tools, and a trigger

Technically, such an agent consists of three parts. The first is the language model at its core, i.e., the AI that derives sensible next steps from text. The second is a memory outside the model, usually a database. It stores what the agent has done so far, what it has learned, and what is still open. Before each new step, it reads back the most important parts from it.

The third part is tools. These refer to access to the outside world: sending emails, retrieving web pages, changing entries in a calendar. Without tools, the agent could only talk but not actually accomplish anything. You can think of the memory as a lab notebook and the tools as the equipment in the lab. The model is the person who uses both.

That leaves the question of what actually sets the agent in motion when nobody is typing. For this, there are triggers, so-called triggers. That can be a specific time, a new email, or a changed stock price. When the event occurs, the agent wakes up, reads its memory, and decides on the next step. Afterward, it goes back to sleep and doesn’t consume any computing power.

First products and how to recognize them

In practice, persistent agents are found today mainly in programming. Tools like GitHub Copilot Workspace or Devin receive an error message and then continue working independently. They change files, test the result, and make improvements, often over hours. The human only looks at the result at the end. Similar systems exist for customer service and research.

In the news, they often appear under English terms. There’s talk of agentic AI, autonomous agents, or long-running tasks. Usually the same thing is meant: AI that doesn’t just respond, but acts over an extended period of time. A common misconception is to call every automation this way. A script that runs the same three commands every night is not an agent. What matters is that the system itself decides which step makes sense next.

For users, this means one thing above all: you should know what rights such an agent has. Is it really allowed to spend money, send emails, or delete files? Many providers therefore build in checkpoints where a human must confirm. This principle is called human in the loop. It costs time, but it prevents a single reasoning error from going unnoticed for days.