Multi-Agent Kanban is the Hermes update that turns AI agents from solo workers into a proper team you can manage from one board.
Instead of opening different terminals and trying to remember which agent is doing what, you can put tasks into one workflow and let the agents move through them.
The AI Profit Boardroom is a place to learn practical AI agent workflows when tools like Hermes start changing how real business tasks get done.
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Multi-Agent Kanban Makes Hermes Easier To Control
Multi-Agent Kanban matters because AI agents get messy fast when you try to run more than one task at a time.
Before this kind of workflow, you either had to wait for one agent to finish or open several terminals and manage everything yourself.
That sounds fine for one small test, but it becomes painful when you have research, writing, review, support, and admin work running together.
You lose track of the task.
You forget which agent has the latest context.
You waste time checking windows instead of checking results.
Multi-Agent Kanban gives that work one place to live.
The board can show tasks moving through triage, ready, running, blocked, and done.
That simple structure makes the whole agent setup easier to understand.
You can see what is waiting, what is active, what is stuck, and what has already been finished.
This is useful because AI agents only become powerful when you can manage them properly.
A messy agent workflow still creates more work for you.
A clean board gives you more control without forcing you to babysit every step.
Hermes Uses Multi-Agent Kanban As A Real Work Board
Hermes Agent is a free open-source AI agent that runs on your computer and connects with the models you want to use.
It can work with tools like Claude, GPT, Gemini, Kimi, GLM, and other models depending on your setup.
That already makes Hermes flexible.
The bigger change is that Multi-Agent Kanban gives Hermes a proper work system.
Instead of one long chat doing everything, you can create agent profiles for different roles.
One profile can handle research.
Another can handle writing.
A third can review the output.
Each profile can have its own setup, tools, memory, and model if you want to separate things properly.
That makes the workflow feel more like a small team than a single assistant.
The board gives every task a clear place.
The agents know what they are supposed to do.
You get a cleaner way to manage the work without jumping between disconnected chats.
That is why this update feels bigger than a normal feature drop.
It changes how you use Hermes.
The Dispatcher Makes Multi-Agent Kanban Work
Multi-Agent Kanban works because Hermes uses a dispatcher to manage the board.
The dispatcher checks for tasks that are ready.
When it finds one, it launches the right agent profile and gives it the task.
The agent reads the card, checks the comments, looks at the context, and starts working.
When the job is finished, the result goes back onto the card.
If the agent gets stuck, the task can move to blocked and wait for your input.
That is a much cleaner workflow than trying to watch several agents manually.
You are not guessing where things are.
The board shows the current state.
The dispatcher handles the movement.
The agent handles the task.
You step in when judgment, approval, or direction is needed.
This is the part that makes Multi-Agent Kanban feel practical for real work.
It is not just a pretty board.
It is a system for moving tasks through different agent roles without losing the thread.
Parallel Work Is The Big Multi-Agent Kanban Upgrade
Multi-Agent Kanban becomes powerful when agents start working in parallel.
This is not just switching between tabs and pretending things are moving together.
The agents can run as separate processes on your machine.
That means one agent can research while another writes and another reviews.
You do not have to sit there waiting for task one to finish before task two can begin.
That changes the speed of the whole workflow.
A researcher can gather notes.
A writer can turn those notes into a draft.
A reviewer can check the draft before it reaches you.
The board keeps those steps organized so the agents do not trip over each other.
This matters because most people still use AI like one assistant answering one question at a time.
Multi-Agent Kanban lets you think more like a manager.
You assign work, let the system run, and come back to check progress.
That is a major shift.
The goal is not to prompt harder.
The goal is to build a workflow where multiple agents can move work forward without you manually pushing every step.
Comments Give Multi-Agent Kanban Better Memory
Multi-Agent Kanban has one simple feature that makes a big difference.
Every card has a comment thread.
That comment thread can be read by agents and humans.
This matters because context is where many AI workflows fall apart.
A researcher might find useful notes, but the writer starts from zero because the notes were trapped in another chat.
A reviewer might miss why a decision was made because the reasoning was buried somewhere else.
With Multi-Agent Kanban, the context stays on the card.
The researcher can leave notes.
The writer can pick them up later.
The reviewer can see the full trail before checking the work.
None of them need to be online at the same time.
That is the useful part.
The board becomes the shared memory for the task.
You can read the comments, check the handoffs, fix bad context, and understand what happened.
That makes the workflow easier to trust.
It also makes the agents feel less forgetful because the important task context is sitting where everyone can see it.
Workspaces Keep Multi-Agent Kanban From Getting Messy
Multi-Agent Kanban also uses workspaces to keep tasks cleaner.
Each task can have its own folder or scratch space.
That gives the agent a contained place to work.
This is important because agents can create files, pull notes, draft content, run checks, and generate outputs.
Without a clean workspace, that work can spread across your machine and become hard to manage.
A workspace keeps the task separated from everything else.
When the task is done, you can choose whether to keep that folder as a record or clean it up.
That gives you more control over the mess.
This is especially useful when you run several agents at once.
Each task needs its own space.
Each output needs to be easy to find.
Each agent needs boundaries.
Multi-Agent Kanban makes that structure easier to manage.
It stops your AI workflow from becoming a pile of random files, half-finished drafts, and unclear outputs.
Task Trees Make Multi-Agent Kanban Better For Bigger Projects
Multi-Agent Kanban becomes much more useful when you start using task trees.
A task tree lets one big job break into smaller related jobs.
For example, one research task can create five smaller research tasks.
Different researchers can run those jobs at the same time.
Then an analyst can combine the notes.
After that, a writer can turn the research into a final draft.
Then a reviewer can check the final output.
The dispatcher can wait until the parent tasks are finished before moving the next step forward.
That stops the writer from starting before the research is ready.
It also reduces duplicate work.
This is close to how a real team works.
People do different parts of the job, then pass the work forward when it is ready.
Multi-Agent Kanban brings that team structure into Hermes.
That matters for bigger workflows like content production, lead research, client reports, support queues, meeting summaries, and project planning.
You are no longer stuck with one giant prompt doing everything badly.
You can split the work into cleaner steps.
Client Work Gets Cleaner With Multi-Agent Kanban
Multi-Agent Kanban can also separate tasks by client or project.
That matters if you manage work for more than one business.
You do not want client A’s notes mixing with client B’s drafts.
You do not want one agent pulling the wrong context into the wrong task.
You need separation.
Hermes can tag tasks by tenant, which basically means client, project, or workspace.
That makes the system more useful for agencies, consultants, freelancers, and operators managing multiple workflows.
The same agents can work across different projects, but the context can stay separated.
That is important for practical business use.
AI workflows become risky when boundaries are unclear.
A clean board with project tags makes the work safer and easier to review.
This is where Multi-Agent Kanban starts to feel like a serious business tool.
It is not only about running cool demos.
It is about keeping daily work organized when multiple agents, clients, and tasks are involved.
Multi-Agent Kanban Works For Daily Business Tasks
Multi-Agent Kanban is easy to understand when you apply it to daily work.
Imagine you get five customer questions every morning.
Instead of answering each one manually, you add each question to the board as a task.
A support agent drafts the reply.
Another agent checks the tone and accuracy.
The card waits for your approval.
You review, tweak, and send.
That is a much better workflow than starting from scratch every time.
The same structure can work for content planning, lead research, meeting summaries, inbox drafts, sales prep, and client reporting.
The board gives each task a place.
The agents handle the first pass.
You make the final call.
That is the practical value of Multi-Agent Kanban.
It does not remove you from the workflow.
It moves you to the part where your judgment matters most.
The AI Profit Boardroom helps you learn how to build these agent workflows for real business tasks instead of guessing through setup alone.
Durability Makes Multi-Agent Kanban More Reliable
Multi-Agent Kanban is useful because the work can survive beyond one chat session.
Older delegated tasks were helpful, but they were temporary.
Once the chat ended, the record could disappear or become hard to reuse.
The Kanban board is different.
Tasks stay on the board.
Comments stay on the card.
The history stays readable.
If your laptop closes, the board is still there.
If Hermes restarts, the work can continue.
If your computer crashes, the data can still sit in a local file and pick up later.
That changes what you can trust agents to do.
You can leave a workflow running longer.
You can build a record of decisions.
You can come back later and still understand what happened.
That durability matters when AI work becomes part of your daily operations.
A workflow that disappears when a chat ends is useful for quick tasks.
A workflow that survives restarts is much better for real projects.
Multi-Agent Kanban Changes Your Role From Worker To Manager
Multi-Agent Kanban changes the way you think about using AI.
With a normal chatbot, you are still the person pushing every step forward.
You ask.
You wait.
You copy.
You paste.
You correct.
You ask again.
That can help, but it still feels like manual work with extra assistance.
Multi-Agent Kanban moves you into a manager role.
You create the task.
The board organizes the work.
The agents handle the smaller steps.
You review the output.
That is a different relationship with AI.
You are not using one assistant for one answer.
You are managing a small system of workers.
This is the real shift inside Hermes.
The agents do not need to be perfect for the workflow to be useful.
They need a structure that lets them work, hand off context, wait for help, and continue without losing everything.
Multi-Agent Kanban gives them that structure.
Multi-Agent Kanban Is Powerful But Still Technical
Multi-Agent Kanban is exciting, but it is not fully beginner-friendly yet.
You still need to be comfortable with terminal commands.
You need to set up profiles.
You need to install and understand the gateway.
You need to learn how the board and dispatcher work together.
That means some people will hit friction at the start.
This is not a polished point-and-click app yet.
It is more like a power tool for people who are willing to set things up properly.
That is not a bad thing.
It just means expectations need to be realistic.
The reward is that once the system is working, the workflow becomes much cleaner.
You get one board instead of several messy terminals.
You get agent roles instead of one overloaded chat.
You get task history instead of lost context.
For serious users, that setup time is worth it.
Hermes Adds More Than Multi-Agent Kanban
Multi-Agent Kanban is the headline feature, but the Hermes update includes more than the board.
There is also an autonomous skill curator.
That background agent helps clean up the skill library over time.
Old skills can be removed.
Duplicate skills can be merged.
That makes the system easier to maintain as it grows.
Startup time also improved, which matters if you use Hermes every day.
Waiting on tools to load becomes annoying when you are testing workflows often.
There is also a Google Meet integration.
Hermes can join a meeting, turn on captions, capture a transcript, and send you a summary afterward.
That turns meetings into another workflow the agent can support.
You can review the notes later and act on the decisions.
Together, these updates make Hermes feel more like an operating layer for AI work.
The board manages tasks.
The curator keeps skills cleaner.
Meeting support captures useful context.
That combination makes the update feel bigger than one feature.
Multi-Agent Kanban Shows The Future Of AI Work
Multi-Agent Kanban points toward where AI workflows are going.
The future is not one chatbot answering one question.
The future is multiple agents working through structured systems.
The board manages the work.
The agents handle different roles.
Comments preserve context.
Workspaces keep files clean.
Task trees break down bigger projects.
Tenant tags separate clients and projects.
That is a stronger foundation for real automation.
Six months ago, many people still had to babysit agents constantly.
They watched every step, repeated instructions, fixed memory problems, and cleaned up the mess afterward.
Now the workflow is moving toward agents that can manage more of the process themselves.
You still stay involved, but you do not need to push every tiny step.
That is why this update matters.
Multi-Agent Kanban makes AI feel less like a tool you keep prompting and more like a workflow you can manage.
The AI Profit Boardroom gives you a place to learn Hermes, Multi-Agent Kanban, and other AI agent systems with practical workflows you can use in real business tasks.
Frequently Asked Questions About Multi-Agent Kanban
- What is Multi-Agent Kanban?
Multi-Agent Kanban is a board-based AI workflow where multiple agents can pick up tasks, work in parallel, hand off context, and track progress. - How does Multi-Agent Kanban work in Hermes?
Hermes uses a dispatcher to check the board, launch the right agent profile, assign tasks, update cards, and move work through the workflow. - Why is Multi-Agent Kanban useful?
It is useful because it lets several AI agents work side by side instead of forcing you to manage one task at a time. - Does Multi-Agent Kanban keep task history?
Yes, each card can keep comments, handoffs, updates, and workspace context so agents and humans can understand what happened. - Is Multi-Agent Kanban beginner-friendly?
It is powerful, but it still requires terminal setup, agent profiles, and gateway configuration before it feels smooth.