Vibe Kanban Alternative for AI Agent Workflows

Mrrlin is a Vibe Kanban alternative for teams that want AI tasks, execution runs, decisions, and evidence connected to one goal.

Chat tools vs Mrrlin

The execution layer beats another blank chat window.

Chat tools make you re-send context and manage the work. Mrrlin keeps project memory and spends tokens deliberately.

Chat-based AI tools
Mrrlin
Token cost
Full context re-sent with every prompt
Progressive context compression — the fewest tokens per task
Project memory
Forgets between sessions — you re-explain and re-instruct
Memory is captured continuously — context stays attached to the work

Why it matters

A card in the done column should come with proof that it is done.

Mrrlin combines a visible board with specs, inbox questions, runs, artifacts, and operator approval loops.

01After triaging 40 support tickets into fixes, the board fills up fast. The hard part is not moving cards; it is knowing which of the finished ones were verified and which only reported success.
02Teams weighing a Vibe Kanban alternative usually already like working from a board. What they are deciding is how much of the surrounding work, from the spec behind a card to the review that accepted it, should live in the same place.
03Parallel agents make a board busy in ways that are hard to read. Ten cards in progress can mean ten healthy runs, or three healthy runs and seven agents stuck on the same unanswered question.
04Approvals often happen beside the board, in a chat message or a quick call. When a fix ships, the card holds no record of who said yes, what they looked at, or which risks they accepted.

Workflow

How a support-ticket fix moves across the board with evidence.

01

Turn tickets into scoped cards

Paste the triaged ticket notes and state the goal: fix the ten most reported checkout errors this sprint. The Director groups duplicates, writes one task per fix with acceptance checks, and sets impact and dependencies so the board opens in a sensible order.

02

Let Autopilot pull the next card

Autopilot advances tasks unattended inside a policy you set: cadence, a daily job budget, and a cap on concurrent runs. Cards marked for human review stop and wait for you, while the routine fixes keep moving through their runs.

03

Watch the run behind each card

A card's execution runs show a status such as running, self-review, pull request, or failed, along with the worktree, branch, test results, and any question the agent raised. An in-progress card tells you what is actually happening underneath it.

04

Accept on evidence, not movement

Finished fixes sit in awaiting verification until a separate reviewer and a receipt confirm them. A card that cannot produce a green build or a working address stays open, or closes with no result, instead of drifting into done.

Use cases

Where it fits.

AI task boards

A growth team's board holds the week's work: two landing-page tests, a copy refresh on the plans page, and a signup bug. The Director creates the cards from one goal, and each carries goal notes, an assignee, a due date, impact, and what it is blocked by.

Agent progress tracking

With five agents running at once, the board shows which runs are in self-review, which opened a pull request, and which failed. The Autopilot policy can stop work after repeated failures, so a broken card does not keep retrying through the night.

Approval queues

Cards that touch production or customer-facing text arrive in the inbox as approval requests, bundled with the evidence the reviewer saw. You clear a queue of six approvals in one sitting instead of answering pings scattered across the day.

Evidence review

Before accepting a card, open its artifacts: diff summary, test result, verification notes, and the produced URL. When a fix to the refund flow looks wrong a week later, the card still shows what was checked and who approved it.

Comparison

What a card holds when it reaches the done column.

Usual approach
Mrrlin
Creating the cards
Someone reads every ticket, writes a card for each one, and guesses at priority and order along the way.
The Director writes cards from one goal, merges duplicates, and sets impact and blocked-by links before any agent starts.
Reading progress
On a hand-kept board, a card sits in progress and the only way to learn more is to ask whoever launched the agent.
Each card shows its run status, branch, worktree, and open questions, so progress is readable without interrupting anyone.
Calling it done
When status is updated by hand, a card reaches done the moment an agent reports success or someone drags it there.
A card closes on a receipt, such as a green build or a working address, after a reviewer that did not write the fix has read it.

FAQ

Before you start.

When is Vibe Kanban the better fit?

Vibe Kanban is built around a board for AI tasks and agent work. If a board is what your team needs and your specs, reviews, and approvals already live somewhere that works, it may be the simpler choice. A Vibe Kanban alternative like Mrrlin fits when you want those pieces attached to the same goal as the cards.

Is the Mrrlin board just another kanban view?

The Kanban surface is one part of the workspace. Behind each card sit a written spec, execution runs with their artifacts, reviewer notes, and inbox questions. The Director creates cards from goals and Autopilot can advance them under a policy, so the board reflects the work rather than being the only place it is tracked.

How should we compare a Vibe Kanban alternative on real work?

Put the same batch of work, such as ten support-ticket fixes, through each option. Then pick a finished card at random and ask what it can show you: the spec, the run, the test results, the reviewer's notes, and who approved the release. Those answers usually settle the decision.

Tell us the outcome you want AI to execute.

Share the workflow you want to automate. We’ll map the first Mrrlin run — plan, agent routing, review loops, and approval checkpoints.

No credit card · No migration · One goal