{"slug": "hermes-bot-mode-i-built-a-team-of-ai-agents-that-hand-off-work-to-each-other", "title": "Hermes Bot Mode: I Built a Team of AI Agents That Hand Off Work to Each Other", "summary": "A developer built a team of three AI agents using the Hermes Bot Mode desktop plugin, creating a Researcher → Risk Analyst → Thesis Editor pipeline that hands off work automatically. The test demonstrated agent-to-agent collaboration where the human defines the objective and specialized agents handle coordination, moving from a human-in-the-loop model to a more autonomous multi-agent workflow.", "body_md": "What if your AI agents behaved less like isolated chatbots and more like a team of specialists that could actually collaborate? 🤖\n\nThat’s what I wanted to test with the newly released Hermes Bot Mode desktop plugin.\n\nInstead of manually switching between different Hermes profiles, copying context, and triggering every stage myself, I built a small team of three AI agents:\n\n**Researcher → Risk Analyst → Thesis Editor**\n\nThen I gave one task to one agent.\n\nThe goal was simple: see whether the agents could gather evidence, challenge each other’s work, and produce a final answer through agent-to-agent handoffs without me manually operating every stage.\n\nAnd it mostly worked.\n\nBut the interesting part isn’t the stock research demo itself.\n\nIt’s what Bot Mode changes about how we interact with Hermes Agent, persistent AI agents, and multi-agent workflows.\n\nHermes already supports profiles.\n\nA profile can have its own configuration, model settings, soul, memory, skills, and tools.\n\nThat means you could already create multiple specialized Hermes agents.\n\nBot Mode doesn’t replace that system.\n\nInstead, it adds a visual usability and orchestration layer on top of Hermes profiles.\n\nThe easiest way I can describe it is:\n\nHermes Profiles give you multiple isolated brains. Bot Mode gives those brains faces, rooms, and a team interface.\n\nInstead of remembering profile names or managing everything through the CLI, you get a visible roster of bots.\n\nEach bot can have its own:\n\n🪪 Name and visual identity\n\n🎯 Specialized role\n\n🧠 Personality and memory\n\n🛠️ Tools and skills\n\n💬 Persistent conversation\n\n🔄 Ability to communicate with other bots\n\nThat last capability is where things get interesting.\n\nI wanted a task where simply generating an answer wasn’t enough.\n\nThe workflow needed different types of judgment.\n\nSo I created three specialized bots.\n\nThe first agent gathers evidence.\n\nIts job is to research two U.S.-listed companies in the same sector using public filings and reputable market data.\n\nFor the demo, I compared NVIDIA and AMD across areas such as:\n\nBut I didn’t want the first agent’s answer to automatically become the final answer.\n\nThat is where the second bot comes in.\n\nThe Risk Analyst is deliberately adversarial.\n\nInstead of expanding the researcher’s conclusions, its job is to attack them.\n\nIt checks things like:\n\nThis creates an important separation between generating research and reviewing research.\n\nFinally, the reviewed material goes to the Thesis Editor.\n\nIts job is not to research everything again.\n\nIt takes the evidence and critique from the previous agents and produces a balanced comparison containing:\n\n📚 Sources\n\n📅 Relevant dates\n\n🎚️ Confidence notes\n\n❓ Known unknowns\n\n⚖️ A more balanced final analysis\n\nNow we have a simple multi-agent pipeline:\n\n**Research → Challenge → Synthesis**\n\nThis was the actual test.\n\nI gave my instructions to the Stock Market Researcher.\n\nThat was my only initial task instruction.\n\nI wasn’t manually taking its output and pasting it into the Risk Analyst.\n\nI wasn’t opening the Thesis Editor and telling it what to do next.\n\nInstead, the researcher gathered its evidence and then attempted to hand the work to the Risk Analyst.\n\nThe Risk Analyst reviewed it and attempted to pass the reviewed material further down the chain.\n\nThe bots were composing and routing these handoffs themselves.\n\nThat’s a much more interesting interaction model than:\n\n**Human → Agent A → Human → Agent B → Human → Agent C**\n\nInstead, we’re moving toward:\n\n**Human → Agent A → Agent B → Agent C → Human**\n\nThe human defines the objective while specialized agents handle parts of the coordination.\n\nOne thing I like about Bot Mode is that these aren’t just invisible background calls.\n\nEach specialist exists as a recognizable bot with its own persistent conversation.\n\nI can open the researcher and inspect what it did.\n\nI can open the Risk Analyst and see what it received.\n\nI can inspect what happened during a handoff.\n\nThat matters when you’re experimenting with multi-agent AI systems.\n\nIf an AI team produces a bad result, you don’t just want the final answer.\n\nYou want to understand:\n\nWhere did the workflow go wrong?\n\nWas the original research weak?\n\nDid the critic miss something?\n\nDid information disappear during a handoff?\n\nDid the final agent overstate the evidence?\n\nMaking the agents and their conversations visible gives you a much better mental model of the system.\n\nCreating a bot from the desktop interface is fairly straightforward.\n\nYou can give it a:\n\nThe advanced configuration is where the specialization becomes more powerful.\n\nYou can configure the bot’s soul, assign skills, control its available tools, or clone it from an existing Hermes profile.\n\nThat means these don’t have to be three copies of the same generic assistant with different names.\n\nYou can design genuinely different specialists.\n\nFor example:\n\n**Research Agent**\n\nWeb access + research skills + evidence-focused instructions.\n\n**Critic Agent**\n\nVerification instructions + skeptical personality + strict rules around unsupported claims.\n\n**Editor Agent**\n\nStrong synthesis instructions + limited mandate to introduce new claims.\n\nThe architecture becomes interesting when the agents have different responsibilities, context, tools, and behavioral instructions rather than simply different labels.\n\nAnother useful feature is scheduled jobs.\n\nYou can configure a bot with an instruction and schedule it to run at a particular frequency.\n\nFor example, the stock research bot could potentially run a recurring morning research task.\n\nThat opens up use cases beyond manually initiated conversations:\n\n📊 Daily market research\n\n📰 News monitoring\n\n🔍 Competitive intelligence\n\n📈 Recurring business analysis\n\n📋 Periodic reporting\n\n🧭 Research updates\n\nPersistent specialists become much more useful when they can perform recurring work instead of waiting for a new chat every time.\n\nThis distinction is important.\n\nIt’s easy to see agents communicating and assume you’ve suddenly built a full multi-agent orchestration system.\n\nThat’s not what Bot Mode currently is.\n\nThe handoffs are real, but they’re per invocation.\n\nA receiving bot might respond later, and a bot already processing something might not be interrupted immediately.\n\nBot Mode also does not guarantee parallel execution.\n\nMy demo was effectively sequential:\n\n**Researcher → Risk Analyst → Thesis Editor**\n\nSo I wouldn’t treat Bot Mode as a replacement for a proper DAG or structured workflow engine.\n\nI actually encountered a failed handoff during the demo.\n\nThe Risk Analyst completed its review but failed to successfully pass the work to the Thesis Editor.\n\nRather than hiding the failure, I kept it in the video because it demonstrates an important limitation.\n\nBot Mode is currently beta release.\n\nFailures can happen.\n\nIn my case, I instructed the researcher that the previous delegation had failed and asked it to retry.\n\nThe researcher then communicated with the Thesis Editor, passed along the reviewed material, and the workflow continued.\n\nFor production-grade autonomous workflows, that distinction matters.\n\nA good agent interface doesn’t automatically give you guarantees around:\n\nThose are orchestration problems.\n\nThese three concepts solve different problems.\n\nHermes Profiles provide the underlying specialization.\n\nThey give agents separate configurations, model settings, memories, souls, tools, and skills.\n\nHermes Bot Mode makes those specialists easier to operate as a visible team.\n\nYou get identities, persistent conversations, a roster, and agent-to-agent communication.\n\nHermes Kanban is still more appropriate when I need a structured project with explicit tasks, dependencies, and organized multi-agent collaboration.\n\nI have used Kanban extensively for more complex AI-agent workflows because those tasks need more structure.\n\nSo I wouldn’t think about Bot Mode as:\n\n“Bot Mode replaces Hermes Profiles or Kanban.”\n\nI would think about it as another interaction model.\n\nProfiles = specialization 🧠\n\nBot Mode = persistent specialists + communication 💬\n\nKanban = structured task orchestration 🗂️\n\nThe right choice depends on the workflow.\n\nMost AI assistants still revolve around one interface:\n\none user ↔ one chatbot\n\nBut many real tasks aren’t naturally one-role problems.\n\nConsider startup research.\n\nYou might want:\n\nMarket Researcher → Competitor Analyst → Skeptic → Founder Memo Editor\n\nFor software development:\n\nArchitect → Developer → Reviewer → QA Agent\n\nFor content:\n\nResearcher → Scriptwriter → Fact Checker → Editor\n\nFor sales:\n\nLead Researcher → Account Analyst → Outreach Writer\n\nThe value isn’t simply having “more agents.”\n\nAdding ten agents to a workflow doesn’t automatically make it better.\n\nThe interesting question is whether we can give specialists clear responsibilities and useful handoffs while keeping the system understandable to the human operating it.\n\nBot Mode is an interesting step toward making that experience more accessible.\n\nI’m particularly interested in where this model goes next.\n\nBecause the bigger opportunity isn’t just giving AI agents better answers.\n\nIt’s giving us better ways to organize, observe, and operate teams of specialized AI agents.", "url": "https://wpnews.pro/news/hermes-bot-mode-i-built-a-team-of-ai-agents-that-hand-off-work-to-each-other", "canonical_source": "https://dev.to/vivek_shetye/hermes-bot-mode-i-built-a-team-of-ai-agents-that-hand-off-work-to-each-other-a49", "published_at": "2026-08-18 15:03:23+00:00", "updated_at": "2026-08-18 15:15:03.274242+00:00", "lang": "en", "topics": ["ai-agents", "ai-products", "ai-tools", "artificial-intelligence"], "entities": ["Hermes Bot Mode", "Hermes Agent", "NVIDIA", "AMD"], "alternates": {"html": "https://wpnews.pro/news/hermes-bot-mode-i-built-a-team-of-ai-agents-that-hand-off-work-to-each-other", "markdown": "https://wpnews.pro/news/hermes-bot-mode-i-built-a-team-of-ai-agents-that-hand-off-work-to-each-other.md", 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