No-Code Agentist

Agentic AI Platform Comparison Most tools being marketed as agentic AI are sophisticated task runners. The difference is whether the agent is measurably better this week than it was last week without you doing anything to teach it. I evaluated seven platforms against that question. Only one passed. Once I learned that scheduled jobs won't run when my laptop is off, I stepped back and took a broader look at Agentic AI options. Microsoft's new Agent Framework is enterprise-grade. Google's ADK is powerful. OpenClaw has genuine potential and the right philosophy, but the implementation is now unreliable, not secure, and missing modern features. These didn't meet every requirement on my list. So what are my requirements? 1. Runs anywhere you want My agent runs on a $75 Raspberry Pi. Not because the Pi is special, but because it proves something: I control where my data lives. I can move it to a VPS, my laptop, whatever. No cloud provider has to approve my deployment. OpenClaw is the closest competitor here. Same philosophy, same local-first approach. However, BEWARE! The creator himself described it as a weekend hack that exploded faster than expected. It started as a quick WhatsApp relay and snowballed into massive GitHub stars. That origin story explains a lot. The potential is real, but the security holes (remote code execution, exposed instances, weak authentication), the unpredictable autonomous mode, and the config mess all scream "built fast, not built right." There's an entire cottage industry of consultancies popping up charging to properly set it up and secure it for clients. That's an economic signal: the duct tape is expensive to live with. 2. Persistent memory When I tell my agent "if you report an error, also suggest the fixes. If there is a straightforward low-risk fix, do it and don't wait for me." Also, "don't post on Sundays" or "I prefer direct language, no fluff," it remembers that next week. Next month. Next year. Every interaction builds on the last. In addition to learning as you use it, you can write up a personality and code of conduct for it to follow. Super cool. Claude Cowork has project-based memory. Close, but sessions start fresh outside those projects. Hermes has memory baked in from day one. OpenClaw does too, but there's a difference between remembering and learning. Hermes doesn't just store what you said. It connects the dots across sessions, builds behavioral models, and adapts. It automatically goes through the transcript of every interaction you have to extract permanent lessons and skills it should retain. Again, super cool. 3. Autonomous scheduling that actually runs when I'm not watching This was the dealbreaker for me. Claude Cowork can schedule tasks. But when I close my laptop, Hermes keeps working. When I travel, Hermes agents all keep doing what I need them to do. When I'm asleep at 2am, Hermes is scanning for security incidents and AI breakthroughs and drafting tomorrow's content. Claude Cowork's schedule is tied to my laptop's power button. Mine is tied to my Raspberry Pi that I never turn off. Cloud platforms are always-on too, but they're not mine. They run on their servers, their rules, their pricing model. 4. Multi-model flexibility I use different LLMs for different jobs. Cheap models for background tasks, expensive ones for complex reasoning. Hermes lets me swap models per cron job, per conversation, per tool. OpenClaw does the same. Claude Cowork? Anthropic only. Period. Anthropic models are, in my opinion, the best, and I use them too, but on Cowork (which I still sometimes use), I hit their usage limits. Most enterprise frameworks support multi-model, but then you're managing the orchestration layer yourself. 5. Goal-oriented, not instruction-oriented Every other tool on this list expects you to tell it what to do, step by step. Hermes lets me state an outcome. I give it a goal and it figures out the plan, the tools, the sequence, and the follow-up. When something changes mid-execution, it adapts without waiting for new instructions. That's the difference between a task runner and an agent. Most of these alternatives are sophisticated task runners. They're fast. They're capable. But they're still waiting for you to write the next step. 6. Self-learning from its own mistakes This is where Hermes pulls away from everything else, including OpenClaw. When my agent fails a task, it doesn't just log the error and move on. It extracts what went wrong, patches its own skills, adds guardrails, and never makes the same mistake twice. Every bug becomes a memory. Every failure becomes a lesson. Every week it's measurably better than the week before. This isn't a feature bolted on. It's the architecture. As I mentioned, Hermes uses goal-oriented action planning with a supervised execution kernel, persistent memory across sessions, and a skill system that lets it author and refine its own procedural knowledge. I love when we finish a major milestone and IT tells ME that it's going to remember how to do that (whatever that was) as this pattern could come up again in the future. OpenClaw checks the same surface-level boxes (self-hosted, multi-messaging, cron, memory) but underneath it's a different generation. No goal-oriented reasoning. No closed-loop learning. No skill system that improves itself. It remembers what you told it, but it doesn't learn from what it did wrong. The agent I'm running today is not the same agent I was running three weeks ago. OpenClaw doesn't do that. Claude Cowork doesn't either. It's excellent at executing tasks, but it doesn't get better at them over time. Google and Microsoft have memory backends, but memory isn't learning. Remembering isn't improving. This is the quiet superpower nobody's marketing yet. The agents that compound are the agents that learn. The comparison table: Runs anywhere you want • Hermes: ✅ • OpenClaw: ✅ • Claude Cowork: ❌ • Vellum: ☁️ • MS Agent FW: ☁️ • Google ADK: ☁️ • Gemini Enterprise: ☁️ Multi-messaging • Hermes: ✅ 8+ • OpenClaw: ✅ 30+ • Claude Cowork: Partial • Vellum: ✅ 3 • MS Agent FW: ❌ • Google ADK: ❌ • Gemini Enterprise: Partial Persistent memory • Hermes: ✅ • OpenClaw: ✅ • Claude Cowork: Project-based • Vellum: ✅ • MS Agent FW: Pluggable • Google ADK: Session • Gemini Enterprise: Memory Bank Autonomous cron • Hermes: ✅ 24/7 • OpenClaw: ✅ 24/7 • Claude Cowork: ⚠️ Needs awake PC • Vellum: ☁️ • MS Agent FW: ❌ • Google ADK: ❌ • Gemini Enterprise: ✅ Multi-LLM/Model • Hermes: ✅ • OpenClaw: ✅ • Claude Cowork: ❌ • Vellum: ✅ • MS Agent FW: ✅ • Google ADK: ✅ • Gemini Enterprise: ✅ Self-learning • Hermes: ✅ • OpenClaw: ❌ • Claude Cowork: ❌ • Vellum: ❌ • MS Agent FW: ❌ • Google ADK: ❌ • Gemini Enterprise: ❌ Goal-oriented • Hermes: ✅ • OpenClaw: ❌ • Claude Cowork: ⚠️ Partial • Vellum: ❌ • MS Agent FW: ❌ Workflows (predefined graphs, not freeform goals) • Google ADK: ❌ • Gemini Enterprise: ❌ Personal agent • Hermes: ✅ • OpenClaw: ✅ • Claude Cowork: ✅ • Vellum: ❌ • MS Agent FW: ❌ • Google ADK: ❌ • Gemini Enterprise: ❌ What I chose and why: I'm not saying these alternatives are bad. They're not. They're solving different problems. Your criteria might be different than mine. I'm one person with a specific job and not an enterprise. Microsoft is building infrastructure for enterprise IT teams. Google is building platforms for AI strategies. Claude Cowork is excellent if your laptop is always open. OpenClaw has the right bones, but it's buggy, insecure, and poorly architected. Note that Hermes' setup and ongoing tuning is more technical than Claude Cowork, whose interface is second to none in ease of use. Therefore this isn't the choice for everyone. However, Hermes is the only tool that checked every box for me: - Runs on hardware I already owned - Reaches me on Telegram at 7am with a daily briefing - Remembers what I said last month - Proactively tells me to draft my next LinkedIn post - Relatively low cost - Works when I'm not watching - Gets smarter every week without me teaching it That last point is the key. This isn't about automation. It's about always-on delegation. My agent isn't a chatbot that does tasks. It's a virtual team that owns outcomes. And it works whether my laptop is open, closed, or in another state. This is exactly how we at Synoptek think about managed services. You don't buy a tool. You hire a team. You don't write scripts. You state goals. You don't control processes. You oversee outcomes. That's the difference between buying software and building an agent. #AIAgents #AgenticAI #BuildInPublic #Hermes #LocalFirst

HERMES AGENT CAN NOW LEARN ANY WORKFLOW YOU SHOW IT. One command turns your knowledge into a reusable skill it remembers across future sessions. The Learn Anything Engine: → Type `/learn` and give Hermes a URL, PDF, document, code folder, notes, or a completed task → Hermes studies the source and creates a structured `skill.md` file → The skill includes the procedure, use cases, version, description, and pitfalls to avoid Why This Matters: ✓ Teach a workflow once instead of re-explaining it every session ✓ Skills load automatically when relevant and work across chat, terminal, Jarvis, dashboards, and messaging apps ✓ Review, edit, delete, or share every generated skill like a normal Markdown file Your workflows no longer have to live inside your head. Show Hermes once. Let it write the recipe. Reuse it forever.

🚀 Do you know Google can ship AI workflows inside Gemini Labs! Think n8n + Zapier + AI agents, but: ✅ No code ✅ Free ✅ Shareable as apps ✅ Multiple AI models in one workflow This isn't the old Gem feature. 🧵 Old Gems: one prompt → one agent → one output. New Gems: inputs → multiple AI steps → structured outputs. You can chain together: 🤖 Gemini for reasoning 🖼️ Imagen for images 🎥 Veo for video All inside a visual editor. Build one in under 5 minutes: 1️⃣ Open Gemini Labs 2️⃣ Create a new Gem 3️⃣ Describe your workflow in plain English 4️⃣ Open Advanced Editor 5️⃣ Customize each step 6️⃣ Share it as an app Example: "Review and analyze contracts like a legal expert." Gemini generates the workflow automatically. The interesting part isn't the feature itself. It's that Google is quietly turning Gemini into an AI operating system for workflows, not just a chatbot. Feels a lot like watching the early days of n8n and Zapier all over again—except the automation nodes are AI models. Have you tried it yet?

While preparing a customer case study, I had one of those weird founder moments. I was talking to one of our most active users and realized the product thesis wasn’t in the pitch deck anymore - it was already running his company. Ardor was always meant to be an open-domain agent with its own cloud. The bet was simple: if an agent has code and resources - databases, deploys, files, logs, terminals, and context, it can solve way more than “coding tasks.” This user has no technical background. He started with: “I need analytics from all marketplaces in one place.” The agent helped build data syncs, a shared database, dashboards, funnel tracking, revenue reports, stock checks, and feedback analysis. Then came: “Warehouse is painful.” The same agent built packing flows, Excel imports, barcode logic, label printing, marketplace supply integrations, and scanner-friendly workflows. Now he’s updating the landing page. Same workspace. Same agent. Product assets, copy, page structure, marketing blocks. That’s the part that hit me. Inside a business, “non-technical” problems quietly become software: data ops, warehouse ops, marketplace ops, BI, landing pages, support, debugging, deployment. A non-tech user is now pushing all of that forward through one agent. Some parts are messy - scripts look like they were named during a fire drill 😅 but the benefits are real: fewer spreadsheets, fewer handoffs, faster internal tools, and one editable place for company operations. This is why Ardor was built open-domain from day one and I still strongly believe in this idea.