Kardbrd Agent
Command AI From Your Board
Write a task. Mention the agent. It picks up the work and delivers results — while your team moves on to the next thing.
Get StartedYou bought AI tools. Now what?
You gave your team AI. Two people started shipping twice as fast. Everyone else kept doing exactly what they were doing before. Same tools, same licenses, wildly different results.
The problem isn't the tools. Each person has to figure out how to talk to AI on their own — what to ask, how to phrase it, how to get something useful back. Some people are naturally good at this. Most aren't. And that's not their fault.
So we made the AI pick up tasks from the board your team already uses.
The board is the interface.
No prompt engineering required.
How It Works
Your team already knows how to write tasks on the board. That's the only skill they need.
Write a task on the board
Describe what you need — a bug fix, a research summary, a first draft. Write it the way you'd explain it to a colleague.
Mention the agent
Tag it in a comment. The agent sees the mention and starts immediately. It's a machine — you command it, it executes.
Work happens
The agent works in its own safe workspace. Progress shows up as comments on the card. You can check in, redirect, correct — or just wait for results.
Review and ship
When it's done, your team reviews what the agent produced. Approve it, tweak it, or send it back with a comment. The human stays in control.
Same AI. Better inputs.
There's no magic here. The agent uses the same model you already have access to on the web. Same Opus, same capabilities. If you sent it a three-sentence request in a chat window, you'd get the same quality back.
The difference is what you give it to work with. A card on the board has a description, checklists, comments, attachments, links to other cards. That's context — real, structured, accumulated context. Not a hasty prompt typed into a chat box.
Garbage in, garbage out. Except with AI it's worse — garbage in, confidently articulated garbage out. You don't want your AI to be a slop-multiplication machine. The board gives people the tools to organize their thinking before the machine touches it.
Ask the agent for help the way you'd ask any contractor — with clear requirements, context, and expectations. The board is just the adequate tool to manage all of that. Made of kardboard.
The card remembers everything
Most AI tools start from zero every time. You explain the problem, give it context, hope it gets it right. Next session — same thing all over again.
The agent works on the card. Every comment, every correction, every "no, not like that — like this" stays there. When the agent comes back to the task, or a different person picks it up, the full history of decisions is already on the card. Not just what was done, but why.
That's planning through conversation, not configuration. The card doesn't just track status — it accumulates reasoning. And that reasoning is what makes the next round of work faster and better.
Your workflow, not ours
Other tools give you a pre-built methodology. Their way of doing things. If it fits — great. If your business doesn't work like a software team, you're out of luck.
Kardbrd is different. You define the lists on the board, you define what each stage means, you define what happens when a card moves. The board is your methodology. The agent follows it.
Software team?
Explore, Plan, Implement, Review. The agent picks up cards and follows the phases you built.
Sales pipeline?
Screen, Analyze, Contact, Sign. Build the lists, build the automation. Same agent, different workflow.
Content production?
Brief, Draft, Review, Publish. The agent writes drafts, your editors review. Cards move left to right.
Something else entirely?
Whatever your process is, build the board to match. The agent adapts to you, not the other way around.
Your business isn't a software team.
Why should your workflow look like one?
What It Actually Gives You
Not features. Outcomes.
Your AI investment pays off
The productivity boost stops being random. Instead of each person figuring out AI on their own, the team writes tasks — something they already know — and the agent handles the rest.
Work gets done overnight
The agent doesn't have office hours. By the time your team comes in the next morning, there's a draft, a fix, or a summary waiting for review. Backlog shrinks without adding headcount.
Zero learning curve
If someone can write a clear task description, they can direct the agent. No new dashboards. No prompt skills. The board is the only interface.
Many tasks at once
A person works on one thing at a time. The agent works on dozens simultaneously. Each task in its own workspace — a mistake on one can't affect another.
Your data stays yours
The agent runs on your machines — a server, a spare laptop, a cloud instance you control. Your code and business logic stay exactly where they are.
Grows with you
Start with one agent on one project. When you see results, add more. No big upfront commitment. You scale when the value is proven.
The gap between AI tools and AI results
Buying AI tools and handing them to employees is like buying a professional kitchen and expecting everyone to become a chef. Some will. Most will make toast.
The agent is the chef. Your team writes the order — what they want, how they want it. The agent does the cooking. The skill required drops from "learn to use AI" to "learn to describe what you need." People are already good at that.
This isn't about replacing anyone. The people who figured out AI on their own will keep doing what they do. Everyone else gets a way in that doesn't require them to change how they work.
Write a task. Get results.
That's the whole pitch.
Open source. Self-hosted. No lock-in.
The agent is free and open source. It runs on your infrastructure — not ours. Your code never leaves your building. You can inspect it, modify it, or stop using it tomorrow.
We run four of these on a single Mac. One for the backend, one for this website, one for the client library, one for the agent itself. Seventy-one tasks in parallel right now. Each one in its own workspace, none of them stepping on each other.
It runs anywhere: a server, a laptop, a Raspberry Pi, a cloud instance. If it can run Python, it can run the agent.
Agent Code & Setup Instructions
Source code, configuration, and setup guide. Clone it, run it, put it to work.
View on GitHub →Make AI work for the whole team.
Not just the few who figured it out.
Get StartedQuestions? [email protected]