{"slug": "hermes-agent-changed-how-i-think-about-ai-agents-from-answer-engines-to-skill", "title": "Hermes Agent Changed How I Think About AI Agents: From Answer Engines to Skill-Building Systems", "summary": "Hermes Agent introduces a new approach to AI agents that focuses on turning individual tasks into reusable skills, shifting the paradigm from answer engines to skill-building systems. The agent operates through a loop of observing, reasoning, acting, and extracting knowledge that can be applied to future tasks, making it more like an operational infrastructure than a temporary assistant. By emphasizing a CLI-first design and a skill layer, Hermes Agent enables developers to preserve procedures and context, transforming repetitive work into reusable assets.", "body_md": "*This is a submission for the Hermes Agent Challenge: Write About Hermes Agent*\n\nThe next leap in AI agents is not just better answers.\n\nIt is reusable experience.\n\nWhen people talk about AI agents, the conversation often starts with automation.\n\nCan the agent use tools?\n\nCan it open files?\n\nCan it run commands?\n\nCan it complete a multi-step task?\n\nThose questions matter.\n\nBut after spending time building with **Hermes Agent**, I think the more interesting question is this:\n\nCan an agent turn one task into reusable knowledge for the next one?\n\nThat shift sounds small, but it changes everything.\n\nIt moves agents from being answer engines to becoming skill-building systems.\n\nMost AI assistants are useful, but temporary.\n\nThey help you solve a task in the moment:\n\nThat is valuable.\n\nBut once the task is done, the learning usually disappears.\n\nThe next time you ask a similar question, the agent starts from scratch again.\n\nThat creates a weird pattern:\n\n```\nHuman learns slowly.\nAgent answers quickly.\nBut the system itself does not get much better.\n```\n\nThe human has to remember the context.\n\nThe repo does not become easier to understand.\n\nThe workflow does not become more reusable.\n\nThe agent helps, but it does not accumulate operational experience in a way that feels productized.\n\nHermes Agent made me think about this differently.\n\nThe most interesting thing about Hermes Agent is not simply that it can use tools.\n\nMany agent systems can use tools.\n\nWhat stood out to me is the idea that an agentic workflow can move through a loop like this:\n\n```\nObserve\n  ↓\nReason\n  ↓\nAct\n  ↓\nExtract reusable knowledge\n  ↓\nUse that knowledge in the next pass\n```\n\nThat last step is the important one.\n\nIf the agent can create or reuse skills, then the system is not only completing a task.\n\nIt is improving the next task.\n\nThat creates a very different product design philosophy.\n\nInstead of building an app that asks:\n\n“What should the agent answer?”\n\nYou start asking:\n\n“What should the agent learn from this interaction?”\n\nThat is a much stronger framing.\n\nHermes Agent feels less like a black-box chatbot and more like a local agentic operating layer.\n\nThe parts that stood out to me were:\n\nThe CLI-first design matters because it makes the agent feel closer to the developer workflow.\n\nDevelopers already live in terminals, repositories, file systems, and local environments.\n\nA local agent that can inspect, reason, and act in that environment feels much more natural than a detached chat window.\n\nA common trap in agent design is thinking that tool use alone makes something agentic.\n\nIt does not.\n\nAn agent that can run a command is useful.\n\nBut an agent that knows **when**, **why**, and **how** to run that command as part of a larger workflow is much more interesting.\n\nThe difference looks like this:\n\n| Basic Tool Use | Agentic Workflow |\n|---|---|\nRun `ls`\n|\nInspect a repository structure |\n| Read a file | Identify architectural areas |\n| Run tests | Understand project verification |\n| Suggest a change | Scope a safe contribution |\n| Complete one task | Create reusable knowledge for future tasks |\n\nThe real value is not the command.\n\nThe value is the reasoning loop around the command.\n\nHermes Agent encourages that loop.\n\nThe most important concept for me was the skill layer.\n\nSkills change the shape of an agentic system.\n\nWithout skills, every interaction is mostly isolated.\n\nWith skills, an agent can preserve procedures, context, and patterns that are useful later.\n\nThat matters because real work is repetitive.\n\nDevelopers do not only solve one-off problems.\n\nThey revisit the same repositories, the same commands, the same architecture, the same testing patterns, and the same contribution flows.\n\nA skill turns that repeated work into a reusable asset.\n\nThat is where agents start to feel less like assistants and more like infrastructure.\n\nMemory is useful.\n\nBut memory alone is not always operational.\n\nA memory might say:\n\n“This repository uses Python and pytest.”\n\nA skill can say:\n\n“When working in this repository, inspect these files first, run this verification flow, avoid this common pitfall, and use this process to scope a first contribution.”\n\nThat is a big difference.\n\nMemory stores information.\n\nSkills store procedure.\n\nAnd procedure is what turns information into action.\n\nWhile experimenting with Hermes Agent, I learned that strong agentic products need five things.\n\nIf the user cannot understand what the agent is doing, the product feels like magic in the bad sense.\n\nThe workflow should be visible:\n\n```\nInput → Agent reasoning → Tool use → Output → Reusable artifact\n```\n\nThe user should know where the agent is in the process.\n\nAgents that can act need boundaries.\n\nA powerful agent without safety rules can become unpredictable.\n\nFor developer tools, that means asking:\n\nThe more capable the agent becomes, the more important the safety model becomes.\n\nA great agentic workflow should leave something behind.\n\nNot just an answer.\n\nA useful artifact.\n\nExamples:\n\nThis is where agentic systems become compounding systems.\n\nThe second pass is underrated.\n\nThe first pass shows that the agent can understand.\n\nThe second pass shows that the agent can improve.\n\nThat is a more powerful story than a single output.\n\n```\nFirst pass: “I understand this.”\nSecond pass: “I can now use what I learned.”\n```\n\nThat is the beginning of agentic learning as a product experience.\n\nDeveloper users need trust.\n\nThey do not necessarily need to see every token or every internal detail, but they do need to see evidence.\n\nGood agent UX should show:\n\nThat visibility turns agent output into something users can trust.\n\nOpen agentic systems matter because developers need control.\n\nIf agents are going to operate in real development environments, developers should be able to understand:\n\nClosed, opaque agent systems can be impressive.\n\nBut open, inspectable agent systems are easier to trust, debug, extend, and integrate.\n\nHermes Agent fits into that direction.\n\nIt gives developers a way to build agentic workflows that feel closer to real software systems than isolated chat sessions.\n\nOne pattern I found especially powerful is:\n\n```\nAnalyze\n  ↓\nGenerate skills\n  ↓\nRun a second pass\n  ↓\nAct safely\n```\n\nThis pattern can apply to many developer workflows:\n\nThe important thing is that the agent does not simply complete a task.\n\nIt creates a workflow that can be reused.\n\nBased on my experience, Hermes Agent is especially interesting when the task requires:\n\nThis makes it a strong fit for projects where the agent is not just answering questions, but operating inside a workflow.\n\nPowerful agents need careful design.\n\nA few lessons became clear very quickly:\n\nLet the agent inspect first.\n\nOnly allow modifications once the workflow is clear.\n\nIf an agent can modify code, isolate the changes.\n\nBlock or review commands like:\n\n```\nsudo\nrm -rf\ngit push\napt-get\nglobal package installs\n```\n\nStructured JSON, tests, diffs, and verification commands make agent behavior easier to trust.\n\nProvider quotas, timeouts, and model errors are real.\n\nA good agentic product should fail gracefully.\n\nThe old way of thinking about AI assistants was:\n\n“How can this model answer my question?”\n\nThe new way of thinking about agents is:\n\n“How can this system complete a workflow, preserve what it learned, and improve the next workflow?”\n\nThat is why Hermes Agent is interesting.\n\nIt points toward agents as systems that can accumulate useful operational experience.\n\nNot consciousness.\n\nNot magic.\n\nJust practical, reusable, developer-controlled experience.\n\nThat is enough to be a big deal.\n\nHermes Agent made me think about agentic development in a more product-oriented way.\n\nThe most exciting agent products will not be the ones that simply generate the longest answers.\n\nThey will be the ones that:\n\nIn other words:\n\nThe future of agents is not just automation.\n\nIt is reusable operational intelligence.\n\nMost agents answer.\n\nBetter agents act.\n\nThe most useful agents learn from action and turn that learning into reusable skills.\n\nThat is the direction I want more developer tools to explore.\n\nAnd that is why Hermes Agent is worth paying attention to.", "url": "https://wpnews.pro/news/hermes-agent-changed-how-i-think-about-ai-agents-from-answer-engines-to-skill", "canonical_source": "https://dev.to/jpablortiz96/hermes-agent-changed-how-i-think-about-ai-agents-from-answer-engines-to-skill-building-systems-1gd2", "published_at": "2026-05-31 02:22:53+00:00", "updated_at": "2026-05-31 02:41:47.057031+00:00", "lang": "en", "topics": ["ai-agents", "artificial-intelligence", "large-language-models", "ai-tools", "ai-products"], "entities": ["Hermes Agent"], "alternates": {"html": "https://wpnews.pro/news/hermes-agent-changed-how-i-think-about-ai-agents-from-answer-engines-to-skill", "markdown": "https://wpnews.pro/news/hermes-agent-changed-how-i-think-about-ai-agents-from-answer-engines-to-skill.md", "text": "https://wpnews.pro/news/hermes-agent-changed-how-i-think-about-ai-agents-from-answer-engines-to-skill.txt", "jsonld": "https://wpnews.pro/news/hermes-agent-changed-how-i-think-about-ai-agents-from-answer-engines-to-skill.jsonld"}}