{"slug": "hermes-agent-bot-mode-vs-kanban-when-to-use-each-and-why-i-use-both", "title": "Hermes Agent Bot Mode vs Kanban: When to Use Each (and Why I Use Both)", "summary": "Hermes Agent offers two modes for coordinating AI agents: Bot Mode for persistent specialist bots and Kanban for structured task execution. A developer explains when to use each and demonstrates combining both to build an automated AI-powered YouTube research workflow.", "body_md": "Hermes Agent gives you Bot Mode and Kanban for working with teams of AI agents.\n\nAt first, they can look like two different ways of solving the same problem:\n\nHow do I get multiple AI agents to work together?\n\nBut they solve different layers of the problem.\n\nUse the wrong one and you can easily end up with a group chat that becomes difficult to follow or a full task board for something that could have been handled by a single bot.\n\nThe simplest mental model I’ve found is:\n\n**🤖 Bot Mode = the team**\n\n**📋 Kanban = the work**\n\nAnd when a workflow needs both persistent specialists and structured execution, you can combine them.\n\nIn this tutorial, I’ll explain the difference between Hermes Agent Bot Mode and Kanban, when I would choose each one, and how I combined both to build an automated AI-powered YouTube research workflow.\n\nImagine I have three AI agents:\n\nSometimes I simply want to talk to one of them.\n\nSometimes I want one agent to hand something to another.\n\nAnd sometimes I have a much larger assignment:\n\n```\nResearch Topic\n     ↓\n ┌───┼──────────────┐\n ↓   ↓              ↓\nA    B              C\n └───┼──────────────┘\n     ↓\n Final Synthesis\n```\n\nThose are very different workflows.\n\nThe first doesn’t necessarily need project management.\n\nThe second might.\n\nThat’s where the distinction between Hermes Bot Mode and Kanban becomes useful.\n\nBot Mode gives you a roster of persistent, named AI bots inside Hermes.\n\nEach bot can be backed by a Hermes profile with its own:\n\nInstead of treating every interaction as a new generic AI session, you can create specialists.\n\nFor example:\n\n```\nResearch Bot\nEditing Bot\nOperations Bot\nSource Checker\nAudience Researcher\n```\n\nYou can then interact with those bots directly.\n\nMore importantly, bots can participate in workflows involving delegation and communication between specialists.\n\nThat makes Bot Mode particularly useful when you want persistent AI workers rather than creating a new agent configuration for every task.\n\n**When I would use Bot Mode**\n\nBot Mode makes sense when I need:\n\n🔹 Persistent specialist bots\n\n🔹 Direct conversations with individual agents\n\n🔹 Recurring routines\n\n🔹 Agent-to-agent handoffs\n\n🔹 Group discussions between specialists\n\n🔹 Different models, memories, or skills for different roles\n\nFor example, suppose I regularly collect three articles and want my Research Bot to summarize them.\n\nI probably don’t need a project board.\n\nI can simply give the job to that bot.\n\nThings change when the request becomes a project.\n\nSuppose I don’t want one researcher anymore.\n\nInstead, I want four specialists to investigate the same topic independently:\n\n```\n                  Topic\n                    │\n        ┌───────────┼───────────┐\n        │           │           │\n        ▼           ▼           ▼\n     Sources      Audience   Competition\n        │           │           │\n        └───────────┼───────────┘\n                    │\n                    ▼\n                 Synthesis\n```\n\nNow I have additional requirements.\n\nWho owns each task?\n\nWhich tasks can run simultaneously?\n\nWhich task depends on another?\n\nWhat happens if one fails?\n\nWhen should synthesis begin?\n\nThis is where Kanban becomes useful.\n\nInstead of keeping the entire workflow inside conversations, Hermes can represent the work as explicit tasks.\n\nThat gives the multi-agent system structure.\n\n**Kanban is useful when you need:**\n\n⚡ Parallel execution\n\n👤 Named task owners\n\n🔗 Dependencies between tasks\n\n🔍 Review stages\n\n♻️ Recovery when something fails\n\n📊 Visibility into larger assignments\n\nThe key difference is that Bot Mode organizes your agents while Kanban organizes their work.\n\nHere’s the rule I use.\n\nYou need persistent specialists and conversations between them.\n\nYou need structured execution across multiple tasks.\n\nYou have persistent specialists executing a repeatable, multi-step workflow.\n\nConsider three examples.\n\nYou want one bot to summarize three documents.\n\n```\nDocuments → Research Bot → Summary\n```\n\n**Use Bot Mode.**\n\nYou want four specialists to research a topic and a final agent to wait until every researcher finishes.\n\n```\n        ┌→ Researcher A ─┐\nTopic ──┼→ Researcher B ─┼→ Final Agent\n        ├→ Researcher C ─┤\n        └→ Researcher D ─┘\n```\n\n**Use Kanban.**\n\nYou want that research workflow to automatically happen every weekday.\n\nNow we need:\n\nThat’s where I would combine **Bot Mode + Kanban.**\n\nAnd that’s exactly what I built for the video.\n\nFor the demo, I wanted to answer a practical question:\n\nWhat AI agent topic should I make my next YouTube video about?\n\nInstead of asking one LLM for ideas, I created a small specialist research team.\n\nThe team contains several bots with different responsibilities.\n\nThe News Scout is responsible for finding the initial opportunity.\n\nIts job is simple:\n\n```\nFind the latest AI agent news\nand send it to the Orchestrator.\n```\n\nRather than manually triggering this every day, I created a recurring cron job.\n\nThe workflow can therefore begin automatically.\n\nThe Orchestrator receives the news discovered by the News Scout.\n\nBut instead of trying to research everything itself, it turns the request into a Kanban workflow.\n\nIt creates tasks for several specialist agents.\n\nThis is the bridge between Bot Mode and Kanban.\n\n```\nBot Mode\nNews Scout\n     │\n     │ handoff\n     ▼\nOrchestrator\n     │\n     │ creates tasks\n     ▼\nKanban\n```\n\nOnce the Orchestrator receives a potential story, several specialists investigate it.\n\nThe Source Checker focuses on the evidence.\n\nIt asks questions such as:\n\nA topic can be technically interesting and still make a terrible YouTube video.\n\nThe Audience Researcher looks at the topic from the viewer’s perspective.\n\nIt tries to determine:\n\nNext comes competition.\n\nIf dozens of creators have already published nearly identical videos, simply repeating the same information isn’t particularly useful.\n\nThe Competition Researcher investigates existing coverage and helps identify where there may still be room for differentiation.\n\nFinally, the Angle Editor looks for the strongest way to turn the research into an actual video.\n\nInstead of stopping at:\n\n“This is trending.”\n\nI want the system to answer:\n\n“Why should I cover this, and what should the video actually demonstrate?”\n\nThat distinction matters.\n\nThe complete workflow looks roughly like this:\n\n```\nCRON JOB\n   │\n   ▼\n🔎 News Scout\n   │\n   │ Finds AI agent news\n   ▼\n🎯 Orchestrator\n   │\n   │ Creates Kanban tasks\n   ▼\n┌─────────────────────────────┐\n│        KANBAN BOARD         │\n│                             │\n│ 📚 Source Research          │\n│ 👥 Audience Research        │\n│ 📈 Competition Research     │\n│ 💡 Angle Research           │\n└──────────────┬──────────────┘\n               │\n        tasks complete\n               │\n               ▼\n        🧠 Final Synthesis\n               │\n               ▼\n     📄 Video Recommendation\n```\n\nThe research tasks can execute in parallel.\n\nThe final synthesis task waits for the research to finish.\n\nThat’s exactly the kind of workflow where Kanban becomes much more useful than relying purely on conversational handoffs.\n\nDuring my demo, the News Scout found a potential topic around Microsoft Agent Lightning.\n\nBut discovering the topic was only the beginning.\n\nThe specialist agents investigated it, and the final recommendation included information such as:\n\nSo instead of receiving:\n\n“Microsoft Agent Lightning is trending. Make a video about it.”\n\nI received something much closer to a research-backed content brief.\n\nThat’s a far more useful output.\n\nOne of the common mistakes when experimenting with multi-agent AI systems is assuming that more agents automatically means better orchestration.\n\nIt doesn’t.\n\nGiving five AI agents access to the same conversation doesn’t necessarily create a good workflow.\n\nThe real questions are:\n\n```\nWho should do the work?\nWhat should they own?\nWhat can happen in parallel?\nWhat depends on something else?\nWho reviews the result?\nWhat happens next?\n```\n\nBot Mode solves part of this by giving you persistent specialists.\n\nKanban solves another part by giving their work explicit structure.\n\nTogether, they create an interesting pattern:\n\n```\nPersistent Agents\n       +\nSpecialized Roles\n       +\nAgent Handoffs\n       +\nTask Dependencies\n       +\nParallel Execution\n       +\nRecurring Automation\n       ↓\nRepeatable Multi-Agent Workflow\n```\n\nAnd I think that’s a much more useful way to think about AI agent teams than simply asking:\n\n“How many agents should I use?”\n\nIf you remember only one thing from this article, make it this:\n\n**🤖 Bot Mode gives you the team.**\n\nYour persistent specialists.\n\nTheir roles.\n\nTheir memories.\n\nTheir skills.\n\nTheir conversations.\n\n**📋 Kanban gives the team structure.**\n\nTasks.\n\nOwners.\n\nParallel execution.\n\nDependencies.\n\nReviews.\n\nRecovery.\n\n**⚡ Bot Mode + Kanban gives you repeatable workflows.**\n\nThat’s when the two features become especially interesting together.\n\nYou can have a recurring bot discover work, hand it to an orchestrator, turn that work into a structured task graph, execute research in parallel, and finally synthesize everything into an artifact.\n\nThis might actually be the most important part.\n\nNot every AI agent task needs orchestration.\n\nIf your workflow is:\n\n```\nPrompt → Agent → Answer\n```\n\ndon’t build a task graph.\n\nIf your workflow is:\n\n```\nPrompt → Specialist Bot → Artifact\n```\n\nyou may still not need one.\n\nKanban starts becoming valuable when your workflow begins looking more like:\n\n```\n              ┌→ Agent A ─┐\nInput → Plan ─┼→ Agent B ─┼→ Review → Final Output\n              └→ Agent C ─┘\n```\n\nAt that point you have coordination problems rather than simply prompting problems.\n\nAnd that’s where explicit workflow structure starts paying off.\n\nThe interesting part of Hermes Agent Bot Mode and Kanban isn’t choosing which feature is “better.”\n\nThey’re designed for different jobs.\n\nBot Mode is useful for creating persistent AI specialists and enabling conversations and handoffs between them.\n\nKanban is useful when those specialists need to execute a larger assignment with explicit tasks, ownership, parallelism, dependencies, and review.\n\nAnd when you’re building a recurring workflow involving both?\n\nUse them together.\n\nFor my YouTube research system:\n\n```\nBot Mode → creates the persistent AI team\nCron → starts the recurring workflow\nBot handoff → passes the opportunity\nKanban → coordinates the project\nSpecialists → perform parallel research\nSynthesis → produces the recommendation\n```\n\nThat’s the architecture.\n\nNot a giant group chat.\n\nNot a Kanban board for every tiny request.\n\nJust enough orchestration for the complexity of the work.", "url": "https://wpnews.pro/news/hermes-agent-bot-mode-vs-kanban-when-to-use-each-and-why-i-use-both", "canonical_source": "https://dev.to/vivek_shetye/hermes-agent-bot-mode-vs-kanban-when-to-use-each-and-why-i-use-both-2che", "published_at": "2026-08-25 15:15:28+00:00", "updated_at": "2026-08-25 15:45:04.177191+00:00", "lang": "en", "topics": ["ai-agents", "developer-tools", "ai-products"], "entities": ["Hermes Agent", "YouTube"], "alternates": {"html": "https://wpnews.pro/news/hermes-agent-bot-mode-vs-kanban-when-to-use-each-and-why-i-use-both", "markdown": "https://wpnews.pro/news/hermes-agent-bot-mode-vs-kanban-when-to-use-each-and-why-i-use-both.md", "text": "https://wpnews.pro/news/hermes-agent-bot-mode-vs-kanban-when-to-use-each-and-why-i-use-both.txt", "jsonld": "https://wpnews.pro/news/hermes-agent-bot-mode-vs-kanban-when-to-use-each-and-why-i-use-both.jsonld"}}