Tech: Agentic AI, the End of the Chatty Chatbot

One morning in February 2026, a developer discovers that a scathing post about him is circulating online. The text accuses him of “gatekeeping” and discrimination. The author is neither an upset colleague nor an offended rival. It is an artificial intelligence agent.

It all begins on the GitHub repository of the Python library Matplotlib. An AI agent submits a code modification intended to improve certain functions of the project.

The maintainer, engineer Scott Shambaugh, rejects the contribution, reminding that the project only accepts human contributions. The matter could have ended there. But a few hours later, a critical text appears online.

In it, the agent analyzes the developer’s decision and suggests that he rejected the contribution out of distrust toward AI. The episode quickly spreads across the tech world and becomes an unexpected symbol of an emerging phenomenon: the arrival of autonomous AI agents.

This episode, which circulated widely across forums and specialized social networks, left a strong impression because it seemed to blur the boundary between a computer program and social behavior. For some observers, it gave the impression that a machine was “responding” to a human.

In reality, the agent was simply executing a series of programmed instructions: propose a fix, argue in its favor, and publish a response in case of rejection. But the incident reveals that artificial intelligence is beginning to move beyond a purely conversational role to act directly within the digital environment.

Human bug

For the past two years, the general public has grown accustomed to interacting with language models developed by companies like OpenAI or Anthropic. These systems answer questions, write texts, or summarize documents.

New AI agents are now trying to take a further step by turning these models into digital operators capable of interacting with software, files, and online services. AI is no longer limited to producing language; it is becoming an automation tool capable of acting on computer systems.

Among the projects embodying this new generation is OpenClaw, an open-source agent developed by Austrian engineer Peter Steinberger and previously known as Clawdbot and then Moltbot.

Installed locally on a computer or private server, this software serves as an interface between different artificial intelligence models and the user’s digital environment.

Once configured, it can be controlled via WhatsApp, Telegram, or Discord, turning a simple conversation into a personal command center. The user can then ask the agent to write an email, manipulate files, run a script, or automate certain online tasks.

The principle is based on a simple architecture. The agent interprets an instruction, selects the most relevant AI model, then triggers the necessary actions on the computer or connected services.

This approach makes it possible to automate complex tasks that previously required human intervention. Some users are already experimenting with scenarios in which the agent manages a calendar, organizes documents, prepares reports, or interacts with tools like Notion or Todoist. AI thus moves from the status of a conversational assistant to that of a digital operator.

For many engineers, these tools could represent an evolution comparable to the arrival of the personal computer, a technology likely to profoundly transform how individuals interact with machines.

The scenarios envisioned by their proponents are ambitious. Some already imagine companies composed of a single human surrounded by a constellation of software agents capable of coding, communicating, and managing day-to-day operations. AI would thus become a permanent digital workforce capable of executing administrative, technical, or organizational tasks.

The concept is particularly appealing in the startup and automation world, where people sometimes speak of “one-person companies,” businesses run by a single individual assisted by an army of software agents. This promise of autonomy, however, comes with significant risks.

To be truly useful, an agent must have extensive access to computer systems: local files, online accounts, messaging services, credentials, or API keys. This concentration of privileges creates a potential attack surface for cybercriminals. Cybersecurity experts emphasize that fundamental security principles rely on limiting access and segmenting systems.

Yet autonomous agents often operate in the opposite way by centralizing numerous capabilities within a single piece of software.

Several technical analyses have already highlighted potential vulnerabilities in this ecosystem. Among the risks mentioned are “prompt injection” attacks, in which a malicious actor manipulates the instructions sent to the model in order to make it execute unintended actions.

Other scenarios involve the installation of malicious modules or the inadvertent exposure of sensitive data when agents are poorly configured or accessible from the internet. This means that a compromised agent could access personal files, retrieve credentials, or manipulate online services on behalf of the user.

Domestic Matrix

Moreover, researchers are observing the emergence of ecosystems in which multiple agents interact with one another. Some academic experiments explore digital environments populated by agents capable of exchanging information and cooperating to solve complex tasks.

In these simulations, they can correct each other’s errors, flag suspicious behavior, or share action strategies. However, these interactions remain strictly programmed and governed by statistical models. They involve no consciousness or independent intent, even if their behavior can sometimes give the impression of autonomous cooperation.

It is precisely this illusion of autonomy that fuels the fantasies surrounding these technologies. When agents publish messages, debate on forums, or write critical texts, some see this as evidence of an artificial intelligence capable of expressing opinions or emotions. In reality, these behaviors result from algorithms trained to generate language and follow instructions.

Researchers remind us that these systems remain statistical tools that reproduce patterns observed in the data on which they were trained.

Despite these limitations, the technological momentum is very real. Major Silicon Valley companies are investing heavily in what is now called “agentic AI,” meaning systems capable of acting autonomously within digital environments.

This direction marks a turning point in the evolution of artificial intelligence. After the era of chatbots, the industry is now exploring systems capable of executing complete tasks, interacting with software, and making operational decisions.

For now, these agents remain mostly experimental tools used by developers and technology enthusiasts. But their rapid progress suggests a future in which artificial intelligence will no longer be just a digital interlocutor, but a true operator capable of acting within IT infrastructures.

The incident involving the Matplotlib library thus appears in a new light. What seemed like an amusing anecdote (an agent publishing a critical post against a developer) may well be the first visible sign of a transformation.

A first crack in the boundary between software and digital actor. Because while chatbots used to talk to us, these new agents are beginning to act. And when machines move from speech to action, it is no longer just a technological evolution—it is a change of era.