Autonomous AI Employee

This is a repost of my article on LinkedIn.

The main dream of modern AI investors is an autonomous AI employee who can fully replace certain specialists and work without any external intervention.

In my view, this is already quite an achievable goal with the current level of technology.

Let me give an example from a field close to me — programming (though this approach is applicable to most digital professions). Let’s build an autonomous AI middle-level backend developer.

We’ll set it up on a local server machine, inside the company’s internal network.

Right away, I’ll say that I will not be sharing my code, configurations, or workflows publicly. My goal is to provide a high-level overview of a solution that works for me, because I see not just a gap, but a complete lack of materials of this kind — even at such a surface level of detail.

Preparation

First, we’ll need a complete Job Description for the employee, the data they need to know, the tasks they need to perform, and the communication channels through which they can interact. Fortunately, remote work has done its job — everyone has learned to communicate digitally.

Next, we formalize access:

• to Git — via SSH keys,

• to Confluence and Jira — via API keys,

• tokens for Telegram, Slack, or other tools (email, or any other communication channels).

We record all of this into a simple JSON configuration file located in the working directory where the employee (agent) will operate — the agent’s root directory.

We install Docker on the server and launch the following services:

• n8n (for workflow orchestration),

• a vector database, and

• a relational database like PostgreSQL.

We also grant n8n access to the working directory.

Configuration

Now we configure communication channels.

In n8n, we add triggers for the required channels so that incoming messages are routed to our agent, who can then return responses.

But instead of calling a regular LLM model, we’ll use a hook or custom node that invokes our local agent.

The Agent

The most complex part is developing the agent itself.

However, a level of “agentness” similar to that of Cursor is already more than sufficient — the key is to craft the prompts methodologically correctly.

Another important step is setting up MCP tools for the agent — such as modules for searching and scraping information, and for working with Jira, Confluence, Git, and so on. This gives our AI employee “hands” and “eyes.”

A RAG (Retrieval-Augmented Generation) mechanism is also essential — for memorizing important things such as:

• how to correctly solve a particular type of task,

• or how to properly find a specific kind of information.

The agent itself can determine what is important using a simple prompt such as:

“Highlight successful solutions from the current session for future memorization.”

This allows our AI employee to accumulate experience.

The relational database is needed to store “behavioral rules,” for example:

• when a supervisor says “don’t do it that way again”, or

• conversely, “do it like this instead.”

Methodology

This is the most important — and probably the most extensive — part of the work: formalizing the working methodology of our employee (ideally, all of this should already be reflected in the Job Description).

The workflow consists of two main stages:

1. Populating the RAG with key knowledge — essentially, we “load” the onboarding process into the RAG.

2. Scheduling — in n8n, we configure cron triggers so that, for example, at 9 a.m. the agent:

- logs into Jira,

- checks for new tasks assigned to them,

- analyzes them, and

- either starts working on them or leaves a comment.

How It All Works

Let’s look at an example with our AI middle developer:

1. Logs into Jira according to schedule and pulls assigned tasks.

2. Checks each task for completeness and feasibility — if something is missing, leaves a comment.

3. Verifies the required environment — network access, code access, Git permissions, etc.

4. Executes the task.
   
   With a well-written description, Cursor already handles 100% of middle-level developer tasks for me (if yours doesn’t — either the context completeness or prompt quality is lacking).

5. Tests locally using Playwright MCP or Postman.

6. Pushes the result to dev, verifies the deployment pipeline.

7. If there are errors — comments in Jira, possibly messages the DevOps engineer, and rolls the task back.

8. If all goes smoothly — rechecks on dev.

9. Moves the task forward, comments in Jira, team chat, or other configured channels.

10. Instead of daily stand-ups, can automatically:

- compile info from **Jira** about yesterday’s tasks,

- pull commits from **Git** related to those tasks,

- send a daily report, and

- note failed tasks and explain why.

1. Can reply to messages in Telegram, Slack, and other communication channels.

2. Can even, based on memorized experience and code analysis, advise junior developers or testers.

At this point, what we have is already a fairly reliable and high-quality middle-level developer, better than the average on the market.

Conclusion

Of course, much depends on the model used by the agent — but nothing prevents us from experimenting with different options to find the optimal balance between quality, cost, and speed.

It is also very important to carefully design the methodology of operation, to account for various behavioral scenarios of the model.

We should not forget about safeguards — to prevent the agent from looping or drifting into endless, meaningless reasoning.

However, if the agent is well designed, those safeguards will already be in place.

Even though this is a pseudo-architecture, I have already managed to run many of the described components locally.

That’s all for now — thank you for your attention.