Automation
An AI agent builder that leaves you owning working software
An AI agent builder should give a department more than a chat window. AgentCode takes the job you describe, writes the code behind it, and hands back a small web application your team opens every morning.
You describe the work · the agent writes the code · your team reviews before anything goes live
In short
An AI agent builder is a tool for creating software agents that carry out a job on your behalf, rather than answering a question and stopping. Most agentic AI platforms let you assemble an agent from prompts, tools and memory, and that agent does the work again on every run. AgentCode works one level down. The agent writes the code for the job once, you approve it, and what runs afterwards is your own application: predictable, cheap to operate and yours to keep.
What you get from the builder
- A working application, not a prompt you have to keep tuning
- Screens your department uses without training or a technical guide
- Code in your repository, readable by anyone you choose to show it
- A plan before any file changes, so nothing happens unread
- Your tests run on every change the agent proposes to you
- A second, third and tenth app built exactly the same way
A process from HR
The problem
New starters are tracked in a spreadsheet, and the equipment, accounts and training rows are updated by three different people who each forget a column.
What the agent builds
The agent builds an onboarding app where every new starter is one case, with a checklist per team and a reminder when a step has been sitting too long.
What you end up with
One page showing every starter and what is outstanding, plus the code, so HR can change the checklist by describing the change rather than filing a request.
What an AI agent builder is for
An AI agent builder is a way to create software that acts, rather than software that only answers. The category grew out of chat assistants: give a model some tools, a goal and somewhere to keep notes, and it can complete a job of several steps. Used as an AI automation platform inside a company, the question gets narrower. Which job, who checks the result, and what is left once the job is done.
- Goal: the outcome you want, written the way your department would say it
- Tools: the systems the agent is allowed to read from and write to
- Boundaries: what it must never do alone, and where a person signs off
- Output: the thing you still have after the run finishes
That last point separates an agentic AI platform that rents you behaviour from one that leaves you with an asset. AgentCode is built around the second answer, which is why the output is code and a running app.
Building the app, instead of being the app
There are two ways an agent can help a department. It can do the work itself on every run, which means each run costs money, takes as long as the model takes, and can differ from the last one. Or it can write the software that does the work, once, after which the software runs on its own.
AgentCode is built around the second. You describe the job, the agent reads your existing code, proposes a plan, writes the files, runs your tests and stops for approval. What you operate afterwards is ordinary software.
- The same input produces the same output, on every run
- The cost of running the app does not grow with how often your team uses it
- Your people can read the rules and ask for a change in one sentence
The result takes the shape of a web app or an internal tool, depending on who opens it.
What HR teams build first
HR runs on processes that repeat with small variations, and on information that has to be gathered from people who are busy. That combination is what keeps the spreadsheets alive, and it is where a small app removes the most friction.
- Onboarding: one case per starter, a checklist per team, and a view of what is outstanding
- Absence requests: a form, a rule about cover, and an approval that reaches the right manager
- Document collection: a list of what is missing per person, chased without anyone nagging
- Probation and review dates: tracked as data rather than as calendar reminders
- Internal referrals: a simple form, a queue, and a status the referrer can actually see
Each is a separate conversation with the agent and a separate small app, and companies usually end up with a shelf of them. For processes that cross into other departments, read business process automation. See pricing for what a plan includes.
Questions teams ask
Do we need to know anything about prompts?
No. You write a description of the job the way you would brief a colleague: what comes in, what should happen to it, who checks the result. The agent asks about the parts that are unclear. Nobody on your side has to learn a prompt format or a modelling language.
How is this different from an agentic AI platform?
Most platforms in that category configure an agent that performs the work on every run. Here the agent performs the build, once, and the result is your own application. You get repeatable behaviour, a running cost that does not grow with usage, and code you can read and keep.
Can one company run several of these apps?
That is the normal pattern. A department starts with the process that hurts most, then builds the next one the same way, and after a few months the company has a shelf of small apps that each do one job well. They all live in your own repository.
What happens if we stop our subscription?
The applications are yours. They live in your repository, run in your environment, and keep working without anything from us. What a subscription buys is the agent that builds and changes them, so you would lose the ability to describe a change and get it written.
More the agent builds
Describe the first job for your agent
Tell it what your team does by hand every week, and get the app back.