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Commit: 2c47ab1 - easy-learn-ai, updated 2026-04-07Imagine two apprentices.
The first, Hermes, doesn't just complete tasks—he observes which steps can be optimized, writes the optimized method into a little manual, and reuses it next time. Over time, he organizes these manuals into a system and keeps upgrading it.
The second, Claw, has a thick martial-arts scripture—every page a distilled technique from past masters, specifying exactly when to use which move. He trains strictly by the book, perfecting everything inside it and touching nothing outside it.
This is not a wuxia novel—it's the real divide in the 2026 AI agent landscape.
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Two Competing Routes
Nous Research's recently released Hermes Agent has sparked intense community debate about what an agent should look like.
Hermes's core pitch: self-generated skills + persistent memory. When asked to make an educational animation, it learns how to use Manim (a math animation library) by itself and saves that skill. Next time, it reuses the learned skill instead of starting from scratch.
This contrasts sharply with OpenClaw's "human-written skills" model—developers write Python tool functions with defined inputs and outputs, and the agent calls these predefined tools. The gateway control plane, integration specs, and business model all rest on human design.
Community comparisons: Hermes has a lower barrier to entry and less skill tuning—a truly "self-learning" agent—while OpenClaw is more of a "tool orchestration platform," where agent intelligence lies in invoking human-designed toolchains.
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The Core Question: Who Defines Tools?
Hermes: the agent defines its own tools
By observing task execution, Hermes identifies recurring tool combinations and automatically generates reusable skill scripts—like a human naturally building a workflow through experience.
Advantages:
- No need for humans to pre-write every possible tool combination
- Adapts to scenarios humans never anticipated
- Each agent can develop its own "personality" and expertise
- Uncontrolled quality of auto-generated skills
- Harder debugging and auditing
- Black-box problem—you don't know why the agent made a decision
- Strong explainability—every step is clear
- Auditable—problems can be traced to specific tools
- Controllable—humans precisely define what agents can and cannot do
- Limited flexibility—no predefined tool means no task
- Heavy manual effort to write and maintain skills
- Poor handling of edge cases and novel tasks
Risks:
OpenClaw: humans define tools, the agent orchestrates
Closer to traditional software engineering—tools are clearly defined interfaces with inputs, outputs, and error handling. Agent intelligence lies in understanding intent and decomposing tasks into tool-call sequences.
Advantages:
Limitations:
Real-World Reactions
Hermes supporters argue this is what real "intelligence" means—a self-improving, adaptive system. OpenClaw, they say, is still "human remote control." True autonomy requires the ability to create tools.
OpenClaw supporters counter that reliability and predictability are the core production requirements. No enterprise would plug a self-rewriting AI into core systems—who takes responsibility when things break? Hermes's "self-evolution" is a cool demo, far from production-ready.
A third view: the future may be layered—human-defined reliable tools at the bottom, agent-composed and optimized skills on top—just as humans use programming languages (predefined tools) to write programs (custom skills).
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The Data Flywheel
Beyond technical routes, the two camps differ on data strategy.
Interesting tools have emerged, such as pi-share-hf—it packages coding agent sessions into Hugging Face datasets with basic anonymization. Baseten has also argued for training and fine-tuning models directly on real production traces.
This reveals a key trend: agent competition is shifting from "whose model is stronger" to "whose data flywheel spins faster."
Hermes's self-evolving model naturally generates skill-creation data—every newly learned skill is a valuable training sample, forming a self-reinforcing loop if collected effectively. OpenClaw accumulates data more systematically through its gateway control plane, gathering tool-call statistics to optimize tool design and agent decision policies—a more "engineering-driven" approach.
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Final Thoughts: Tools Are Power
Throughout computing history, every debate over "who defines the interface" has reshaped technology's direction. Operating systems freed apps from talking to hardware directly; high-level languages replaced assembly; frameworks let developers stand on predecessors' shoulders. Each abstraction layer was a new answer to the tool-definition question.
Hermes represents the more radical vision: AI fully takes over tool definition; humans just state needs. OpenClaw represents the pragmatic path: gradually releasing AI's capabilities within a predictable, controllable framework.
Which route wins? Perhaps both—in different scenarios, in different forms.
But one thing is certain: whether agent tools are self-generated or human-designed, success ultimately depends on whether they truly solve problems for humans.
After all, an apprentice's skill is only proven in real combat.