Multi-Agent Kanban is the update that makes Hermes feel less like one AI assistant and more like a real team working from one board.
Instead of running one task at a time or juggling several terminals, you can now drop work onto a board and let agents pick up tasks in parallel.
The AI Profit Boardroom is a place to learn practical AI agent workflows when updates like this start changing how business automation actually works.
Watch the video below:
Want to make money and save time with AI? Get AI Coaching, Support & Courses
👉 https://www.skool.com/ai-profit-lab-7462/about
Multi-Agent Kanban Makes AI Work Easier To Manage
Multi-Agent Kanban matters because most AI agent workflows are still too messy.
You ask one agent to do one thing, then wait.
After that, you ask another task and wait again.
Some people open several terminals to run multiple agents at once, but that quickly becomes hard to track.
You forget which agent is doing what.
You lose the context.
Tasks get buried in different windows.
Multi-Agent Kanban fixes that by putting the work onto a shared board.
It works more like the project boards people already understand.
There are columns like triage, ready, running, blocked, and done.
You can drop a task into the board, assign it to the right agent profile, and let the system manage the movement.
That makes the workflow easier to see.
Instead of staring at several terminals, you can watch the whole process from one screen.
Hermes Agent Turns Multi-Agent Kanban Into A Real Workflow
Hermes Agent is a free open-source AI agent that runs on your computer.
It can connect with different models like Claude, GPT, Gemini, Kimi, GLM, and others depending on how you set it up.
The important part is that Hermes is not locked to one tiny workflow.
It can send messages, run research, manage tasks, join meetings, pull data, and handle local automation.
Multi-Agent Kanban gives that system a much better structure.
Each agent profile can have its own tools, memory, model, and role.
You might have one agent for research.
Another profile could handle writing.
A third profile could review the output.
The board lets these profiles work together instead of acting like separate disconnected chats.
That is the big shift.
Hermes becomes less like one assistant and more like a small AI operations team.
Multi-Agent Kanban Uses A Dispatcher To Move Work
Multi-Agent Kanban works through a dispatcher.
The dispatcher checks the board and looks for ready tasks.
When it finds one, it launches the right agent profile and gives it the task context.
The agent reads the task, checks the comments, works on it, and writes the result back to the card.
If everything works, the task moves forward.
When the agent gets stuck, the card can move to blocked and wait for human input.
That makes the workflow much easier to manage.
You do not need to constantly watch every agent.
The board shows what is ready, what is running, what is blocked, and what is finished.
This is useful because parallel work only helps if you can still understand what is happening.
Multi-Agent Kanban gives you that visibility.
It also makes the workflow feel more like managing a team instead of prompting one chatbot over and over again.
Parallel Agents Are The Big Multi-Agent Kanban Upgrade
Multi-Agent Kanban changes the speed of AI workflows because agents can run side by side.
This is real parallel work, not just switching between chats.
Each worker can run as its own process on your machine.
That matters because one agent can research while another writes and another reviews.
You are no longer stuck waiting for one task to finish before the next task starts.
That changes how you think about automation.
Instead of using AI like a single tool, you can start using it like a team.
A researcher can gather notes.
A writer can turn those notes into a draft.
A reviewer can check the final output.
The board keeps the work organized so the agents do not step on each other.
That is where Multi-Agent Kanban becomes useful for real business workflows.
It reduces waiting and makes handoffs cleaner.
Comments Give Multi-Agent Kanban Durable Context
Multi-Agent Kanban is powerful because every card has a comment thread.
That sounds simple, but it fixes one of the biggest problems with AI agents.
Agents often lose context when work moves between tools or sessions.
With this system, the context stays on the card.
A researcher can leave notes.
A writer can read those notes later.
A reviewer can check what happened before giving feedback.
None of the agents need to be online at the same time.
The work still makes sense because the card holds the history.
This is much cleaner than dumping everything into one long chat.
The memory is attached to the task where it belongs.
You can read it, edit it, check it, and understand why the agent made certain decisions.
That makes Multi-Agent Kanban easier to trust.
It also makes it easier to fix mistakes without losing the whole workflow.
Workspaces Keep Multi-Agent Kanban Cleaner
Multi-Agent Kanban also uses workspaces for tasks.
Each card can have its own folder or scratch space.
That means an agent can work inside a dedicated area without making a mess across your computer.
This is important when agents are writing files, pulling notes, researching topics, or creating outputs.
A messy agent can quickly create messy folders.
Workspaces help keep each task contained.
You can choose whether to clean up the workspace when the task is done or keep it as a record.
That gives you more control.
For client work, this becomes even more useful.
Each project can stay cleaner because the work is organized by task instead of scattered across random files.
Multi-Agent Kanban makes the agent workflow feel less chaotic.
That is exactly what people need when AI agents start doing more than answering simple questions.
Task Trees Make Multi-Agent Kanban More Powerful
Multi-Agent Kanban also supports task trees.
That means one big task can create smaller child tasks.
For example, you could start with one research task.
That task could spawn several smaller research jobs.
Different agents can run those jobs at the same time.
Then an analyst can combine the findings.
After that, a writer can turn the combined notes into a final piece.
The useful part is that the dispatcher can wait until parent tasks are finished before moving the next task forward.
That prevents the writer from starting before the research is ready.
It reduces duplicate work.
It also makes larger projects easier to break down.
This is closer to how real teams work.
One person does research, another organizes it, another creates the final output, and someone else reviews it.
Multi-Agent Kanban brings that same structure to AI agents.
Tenant Tags Help Multi-Agent Kanban Handle Client Work
Multi-Agent Kanban can also separate tasks by tenant.
That just means you can tag work by client, project, or business.
This matters if you handle more than one workflow.
A single agent system could support several clients without mixing everything together.
One task might belong to client A.
Another task might belong to client B.
The same agent profiles can work across both, but the context stays separated.
That is important for agencies, consultants, freelancers, and anyone managing multiple projects.
Without separation, AI workflows can become risky.
You do not want customer notes, content drafts, or project details getting mixed between clients.
Tenant tagging makes Multi-Agent Kanban more practical for business use.
It gives the system a cleaner way to manage multiple streams of work.
That is one reason this update feels much bigger than a simple board feature.
Multi-Agent Kanban Can Automate Daily Business Tasks
Multi-Agent Kanban becomes easy to understand when you apply it to daily work.
Imagine five customer questions arrive in your inbox every morning.
Instead of answering each one manually, you add them to the board as tasks.
A support agent profile drafts responses.
Those drafts go back onto the cards.
You review, edit, and send.
That can turn an hour of manual work into a quick review session.
The same idea works for research, content, lead lists, meeting notes, email drafts, product updates, and client reports.
The board gives each job a place to live.
The agents do the first pass.
You step in when review, approval, or judgment is needed.
That is the real value of Multi-Agent Kanban.
It does not just make AI feel smarter.
It makes AI easier to manage.
The AI Profit Boardroom helps you learn how to build these multi-agent workflows for practical business tasks instead of guessing through setup alone.
Multi-Agent Kanban Survives Crashes And Restarts
Multi-Agent Kanban is also useful because the board is durable.
That means the work does not disappear when a chat ends.
Older delegate-style agent tasks were useful, but they were temporary.
Once the chat ended, the record could be gone.
The Kanban board works differently.
Tasks stay on the board.
Comments stay on the card.
History stays available.
If your laptop closes, the board is still there.
If Hermes restarts, the work can still be recovered.
If the computer crashes, the data sits in a local file and can pick up again later.
That changes how you can use agents.
You can leave workflows running for longer periods.
Agents can check in, update progress, and wait for help when needed.
That makes AI work feel less fragile.
Durability is not flashy, but it matters when you want a system you can actually rely on.
Multi-Agent Kanban Changes Your Role
Multi-Agent Kanban changes your role from worker to manager.
That is the main mindset shift.
With normal AI chat, you are still doing a lot of the work manually.
You prompt, wait, copy, paste, check, and prompt again.
With a board, you can start assigning outcomes instead of controlling every step.
The agents handle the smaller parts.
The board tracks progress.
You review the result.
That is closer to how a small team operates.
A researcher works on one thing.
A writer handles another.
A reviewer checks quality.
They do not all need to work at the same time.
They just need a shared system where work can move forward.
Multi-Agent Kanban gives AI agents that shared system.
That is why this feels like a real shift rather than a small feature update.
Multi-Agent Kanban Is Powerful But Still Technical
Multi-Agent Kanban is exciting, but it is not point-and-click yet.
You still need to be comfortable with terminal commands.
You need to set up profiles.
You need to install the gateway.
You need to understand how the dispatcher and board fit together.
That is where some people will get stuck.
The tool is powerful, but it still expects users to be comfortable with setup.
That does not make it bad.
It just means this is more for people who want control and do not mind learning the process.
The good news is that once the setup is done, the workflow becomes much easier to manage.
The board gives the system structure.
The agents get clearer roles.
The tasks stop living in scattered terminal windows.
For serious users, that trade-off is worth it.
You spend time setting it up once, then use it to manage repeated work.
Skill Curator And Meet Integration Make The Update Bigger
Multi-Agent Kanban is the headline feature, but it is not the only useful part of the update.
Hermes also added an autonomous skill curator.
This background agent helps clean up the skill library over time.
Old skills can get pruned.
Duplicates can get merged.
That helps the system stay cleaner without constant manual maintenance.
Startup time also got faster, which matters if you use Hermes every day.
Small delays add up when you are constantly opening and testing agent workflows.
There is also a Google Meet integration.
Hermes can join a meeting, turn on captions, capture a transcript, and send a summary afterward.
That turns meetings into another workflow the agent can support.
You can read the notes later and act on what was decided.
All of these updates work together.
The board manages tasks.
The curator keeps skills cleaner.
The meeting tool captures decisions.
That makes Hermes feel more like an operating layer for AI work.
Multi-Agent Kanban Shows The Future Of AI Workflows
Multi-Agent Kanban points toward a bigger change in AI work.
Six months ago, many people were still babysitting AI agents.
They had to watch them closely, clean up the mess, repeat instructions, and manage context by hand.
Now the workflow is moving toward agents that manage more of the process themselves.
The board manages the work.
Multiple agents handle different parts.
Comments preserve context.
Workspaces keep files cleaner.
Task trees organize bigger projects.
Tenant tags separate clients.
That is a much stronger foundation for real automation.
The future is not just one smarter chatbot.
The future is a group of agents working through structured workflows.
That is what Multi-Agent Kanban makes easier to see.
It turns AI from a single helper into a managed system.
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 workflow where multiple AI 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 multiple 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, updates, handoffs, and workspace context so agents and humans can understand what happened. - Is Multi-Agent Kanban easy to set up?
It is powerful, but it still requires some terminal setup, profiles, and gateway configuration before it feels smooth.
