Who Defines the Tools? The Agent Evolution Debate Between Hermes and OpenClaw
*Source: Commit 2c47ab1, easy-learn-ai, updated 2026-04-07*
The Two Apprentices
Imagine two apprentices. The first, Hermes, doesn't just complete tasks—he observes which steps can be optimized, writes the optimized method into a booklet, and reuses it next time. Over time he organizes these booklets into an evolving system. The second, Claw, has a thick manual written by masters, covering exactly what to do in each situation. He practices every move to perfection—but does nothing outside the manual.
This isn't a martial arts novel; it's the real divide in the 2026 AI agent landscape.
Two Routes Compared
Nous Research's recently released Hermes Agent centers on self-generated skills + persistent memory. When asked to make an educational animation, it learns how to use Manim (a math animation library) by itself, saves that skill, and directly reuses it next time.
OpenClaw takes the opposite path: "humans write the skills." Developers write Python tool functions with defined inputs and outputs, and the agent calls these predefined tools as needed. Gateway control planes, integration standards, and business models are all built on human design.
Community comparisons note that Hermes has a lower barrier to entry and less skill tuning—it "learns how to use tools itself"—while OpenClaw is more of a "tool orchestration platform" where the agent's intelligence lies in invoking human-designed tool chains.
The Core Question: Who Defines the Tools?
Hermes route: the agent defines its own tools
By observing task execution, Hermes identifies recurring tool combinations and auto-generates reusable skill scripts—like a human naturally accumulating workflow experience.
Pros:
- No need for humans to pre-write every possible tool combination
- Adapts to scenarios humans didn't anticipate
- Each agent can develop its own "personality" and specialties
- Quality of auto-generated skills is uncontrolled
- Debugging and auditing become difficult
- Black-box problem: you don't know why the agent made a decision
- Strong explainability—every step is clear
- Auditable—issues can be traced to specific tools
- Controllable—humans precisely define what agents can and cannot do
- Limited flexibility—cannot handle tasks without predefined tools
- Heavy human effort to write and maintain skills
- Struggles with edge cases and novel tasks
- Hermes supporters see true intelligence in self-improving, self-adapting systems; OpenClaw is still "humans remote-controlling" an executor.
- OpenClaw supporters argue reliability and predictability are what production demands—no enterprise will connect a self-modifying AI to core systems. Hermes's "self-evolution" is a cool demo, far from production-ready.
- A third voice: future agents may be layered—human-defined reliable tools at the bottom, agent-composed and optimized skills on top, like humans using programming languages (predefined tools) to write programs (custom skills).
- Hermes's self-evolution naturally generates skill-creation data—every newly learned skill is a valuable training sample, enabling a self-reinforcing loop.
- OpenClaw, via its gateway control plane, systematically collects tool-invocation statistics to optimize tool design and decision strategies—a more engineering-driven data accumulation approach.
Cons:
OpenClaw route: humans define tools, agents orchestrate
Closer to traditional software engineering—tools are well-defined interfaces with inputs, outputs, and error handling. The agent's intelligence lies in understanding intent and decomposing tasks into tool-call sequences.
Pros:
Cons:
Real-World Feedback
The Data Flywheel
Community tools like pi-share-hf package coding-agent sessions into Hugging Face datasets with basic anonymization, and Baseten argues for training and fine-tuning directly on real production traces. The key trend: agent competition is shifting from "whose model is stronger" to "whose data flywheel spins faster."
Closing: Tools Are Power
Throughout computing history, every "who defines the interface" debate shaped technology's direction: operating systems, high-level languages, frameworks—each abstraction layer re-answered the question of who defines tools. The agent debate may be history repeating.
Hermes represents a more radical vision: AI fully takes over tool definition, humans just state needs. OpenClaw represents the pragmatic path: gradually releasing AI capability within a predictable, controllable framework.
Which path 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 help humans solve problems. After all, an apprentice's skill is proven in real combat.