Last week, I taught a webinar comparing two of the most popular open-source agent harnesses. Here’s my assessment: OpenClaw Is Like a FerrariOpenClaw is fast, flashy, powerful, and fun. It also breaks down every two blocks. That’s not an insult. Ferraris are great. But you do not buy a Ferrari because you want boring dependability. You buy it because you want performance, speed, and the thrill of handling something slightly dangerous. OpenClaw has a large community, lots of skills, and huge momentum. If you are comfortable with terminal commands, GitHub repos, pull requests, cron jobs, and troubleshooting things at 11:47pm, you may love it. I have two OpenClaw agents, and they are powerful. But they are also high-maintenance little lobsters. When OpenClaw works, it feels like having your own personal Jarvis. When it fails, you become the unpaid sysadmin for a digital crustacean. That’s the tradeoff. Hermes Is Like a Toyota CorollaUsing Hermes is different. Hermes is less flashy, but more dependable. It feels calmer. Milder. Less volatile. Where OpenClaw is exciting for technical tinkerers, Hermes feels more suitable for business implementation. Especially now that it has a desktop app, it’s easy to manage the dashboards, kanban boards, and memory, and I find it has better orchestration with sub-agents. A Hermes agent is not just installed – it grows. It learns from workflows, develops skills, remembers useful context, and can become more specific to your business over time. That makes it more interesting for the client work that I do – installing custom AI agents into businesses, to take over real workflows. I think Hermes is much better than OpenClaw for most small-to-medium sized businesses – because if you don’t have an IT department that knows how to troubleshoot in Terminal, you don’t want to deal with becoming a mechanic for your fast little Ferrari. Most SMBs want something that works, reliably – and that’s what Hermes can do. Because businesses do not need a toy that impresses developers. They need something reliable enough to become part of operations. Agent Teams Need ManagementManaging agents is like spinning plates. If you ignore them for too long, some of them wobble. Some fall over. To keep them spinning, they need constant attention. They need roles, boundaries, review cycles, memory pruning, skills, workflows, and oversight. The job of the human operator is not to do all the work. Our job is to remove bottlenecks. When you are managing your AI agents, ask yourself:
That is the work of the AI operator. Beginner AI Agent ExperimentIf you want to understand agents, do not start with a massive business workflow. Start small, and automate a Morning Briefing. If you’re taking the bus, you can do this with Claude Cowork, or OpenAI’s Codex, or Copilot Agents. Use this prompt: Find the latest news from my industry in the last 24 hours and summarize the five most important stories.
Then use this prompt: Rewrite this as a report for a busy executive who only has two minutes to read it.
Finally, use this one: Turn this into a morning briefing skill and deliver it to me daily.
This is how you move from a prompt to a workflow, from the AI Toolbox to an AI Agent. As the creator of Claude Code, Boris Cherny, recently said, “I don’t prompt Claude anymore. I have loops running that prompt Claude and figure out what to do. My job is to write loops.” My SAGE Framework for Building AgentsWhen I design an agent, I use the SAGE framework: Scope – Keep the agent’s job narrow. Wide agents get vague. Narrow agents get useful. Automate – Find the repeatable pieces. If you do it more than twice, it probably wants a workflow. Generate – Create prompts, skills, templates, and systems that create more output later. Evaluate – Review what works, what wastes tokens, what needs human judgment, and what should be improved. The evaluation loop is where the magic compounds. Because the first version of an agent is rarely excellent. But your tenth version can become so useful, you set it and forget it. That frees you up to go work on your next loop. The Big ShiftThe shift from AI assistant to AI agent is not just a technical upgrade. It is an operational shift. You are moving from asking AI to answer questions, to asking AI to run workflows. That means you need new skills:
The people who learn those skills early will have an unfair advantage. Because the future does not belong to people who merely “use AI.” It belongs to people who can manage digital workers. If you want to accelerate your own learning curve, watch this month’s webinar.
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The AI Maturity Ladder (& REPLAY of Hermes vs OpenClaw webinar)
