Hermes Agent Practical Guide: An AI Agent That Gets Smarter the More You Use It

Why Hermes Is Hotter Than Claude Code

Recently in the AI coding community, an Agent framework called Hermes has been rapidly gaining popularity, with buzz even surpassing Claude Code. Both can write code, but the core difference is: Hermes gets to know you better over time, while Claude Code doesn't.

It's important to first understand the fundamental difference between Agent frameworks and traditional AI tools. Traditional AI tools like ChatGPT and Claude are essentially "request-response" systems: the user asks a question, the model answers, and no state is retained after the interaction ends. Agent frameworks, on the other hand, give AI the ability to autonomously plan, invoke tools, and persist memory, enabling it to act like a true "agent" — decomposing complex tasks, calling external tools to execute operations, and storing experiences for future use. This architectural difference means Agents can accumulate capabilities across sessions rather than starting from scratch every time. It's precisely this fundamental design difference that puts Hermes and Claude Code on completely different evolutionary paths.

This isn't a marketing slogan — it's backed by data. According to real-world testing by a Bilibili content creator, by the fourteenth time performing the same task, Hermes's completion speed improved by over 60%, with accuracy still continuing to rise.

The principle behind this isn't complicated: every time you complete a task with Hermes, it automatically stores the experience from that session. The next time it encounters a similar task, it doesn't start from zero — it picks up directly from where the previously stored experience left off. It's like a real assistant that learns and grows from every collaboration.

Hermes's Core Architecture: Skills and Steering Engineering

Many people assume Hermes is powerful because the underlying model itself is smart. But that's not the case. Hermes achieves "getting stronger with use" through two carefully designed engineering systems.

Skills System: Continuously Loading New Capabilities into the Agent

The first system is called Skills. Its purpose is to enable Hermes to continuously acquire new capability modules. Every time you correct its mistakes or teach it a new approach, these experiences are encapsulated as individual "skills" and stored locally.

From a technical implementation perspective, the Skills system involves several key steps: First is Experience Extraction — extracting key decision paths and solutions from the complete context of each task execution. Next is Skill Encapsulation — abstracting extracted experiences into reusable modules, typically stored as structured prompt templates, code snippets, or workflow configurations. Finally, there's Skill Retrieval — finding the most relevant existing skills through semantic matching or keyword indexing when facing new tasks. This mechanism aligns with the concept of "Meta-Learning" in machine learning — the system isn't learning specific knowledge, but learning how to learn and solve problems more efficiently.

This means Hermes isn't a static tool but a dynamically growing intelligent agent. The more you use it, the richer its accumulated skills become, and its efficiency and accuracy in handling similar problems naturally rise.

Steering Engineering: Keeping Capabilities Under Control

The second system is called Steering Engineering, responsible for managing and constraining these capabilities. This is crucial — many agent projects fail not because of insufficient capabilities, but because the more capabilities are added, the more chaotic things become, ultimately leading to the agent "getting dumber the more you add."

This problem is known in the AI agent field as "Capability Degradation" or "Instruction Conflict." As an agent accumulates more rules and skills, different skills may contradict each other — for example, one skill requires Tab indentation for code style while another requires spaces. Without priority management and conflict resolution mechanisms, the agent's behavior becomes unpredictable. This isn't a new problem in the industry — early Expert Systems were abandoned precisely because rule base expansion caused inference efficiency to plummet. Hermes's steering engineering essentially re-solves this classic knowledge management challenge in the LLM era.

Steering engineering solves exactly this problem: it ensures each skill is invoked in the correct scenario, without conflicts or erratic execution.

Skills + Steering Engineering — both are indispensable. Capabilities without governance leads to increasing chaos; governance without capability accumulation means the agent stays stuck in place forever. Hermes does both well simultaneously, and that's where its true competitive advantage lies.

Seven Built-in Tools: Why Hermes Is More Stable Than Claude Code

Hermes comes with seven core built-in tools covering common development scenarios including code writing, file operations, and terminal execution. Compared to Claude Code, Hermes's advantages are reflected in the following areas:

• Experience reuse mechanism: Never repeats the same mistakes; every execution is optimized based on historical experience
• Local deployment: All data and skills are stored on your own computer, giving you complete privacy control
• Higher stability: Steering engineering ensures orderly tool invocation, significantly reducing the probability of random errors

The coordinated operation of these seven tools, combined with the Skills system's continuous learning capability, makes Hermes's long-term performance far exceed that of one-off AI coding tools.

Local Deployment and Feishu Integration in Practice

Local Installation and Deployment

Hermes supports complete deployment and operation on personal computers. The entire installation process isn't complicated — just follow the steps in the official documentation to complete the environment configuration.

Local deployment holds special strategic significance in the current AI tool ecosystem. Mainstream AI coding tools like GitHub Copilot, Cursor, and Claude Code all rely on cloud API calls, meaning your code, project structure, and even business logic are transmitted to third-party servers. For enterprise users and developers working with sensitive code, this is a serious security concern. In local deployment mode, all model inference (or intermediate data from API calls), skill storage, and file operations are completed on the user's own machine — data never leaves the local environment. Additionally, local deployment eliminates dependency on network stability, allowing you to work with your accumulated skill library even in offline or weak-network environments.

Connecting to Feishu: Remote Control from Your Phone Anytime

Hermes also supports integration with instant messaging tools like Feishu (Lark). Once configured, you can issue commands to Hermes directly from your phone through Feishu, having it execute tasks on your computer.

This feature may seem like just a convenience improvement, but it actually represents an emerging "remote Agent control" paradigm. In this model, the AI agent runs as a resident process on your work computer, while the user issues commands at any time through an instant messaging tool serving as a control panel. This architecture is directly aligned with the ChatOps concept from the DevOps field — ChatOps was originally proposed by GitHub in 2013, with the core idea of integrating operations into chat tools, completing deployment, monitoring, and other tasks through conversational interaction. Hermes extends this concept from operations to general AI coding scenarios, enabling developers to drive code production through natural language at any time, from any location.

Imagine this: you're out and suddenly think of a requirement. You pull out your phone, send a message, and Hermes starts working on your office computer. By the time you get back to your desk, the task might already be done.

Self-Evolution Through Error Correction: Hermes's Learning Loop

This is Hermes's most core and valuable feature: it continuously grows from your corrections.

The specific learning loop works like this:

1. Execute task: Hermes completes the task based on currently accumulated skills
2. Accept correction: You identify issues and provide corrections and guidance
3. Experience consolidation: Hermes converts correction content into new skills or optimizes existing ones
4. Reuse next time: Automatically applies updated skills when encountering similar tasks

This loop is structurally highly similar to the core cycle in Reinforcement Learning. In reinforcement learning, an agent adjusts its strategy based on reward or punishment signals obtained through interaction with the environment. In Hermes's loop, the user's correction behavior is equivalent to providing clear "reward signals" — telling the system what's right and what's wrong. The difference is that traditional reinforcement learning requires massive trial-and-error iterations and numerical reward functions, while Hermes leverages large language models' natural language understanding to extract improvement directions directly from users' textual corrections, dramatically lowering the threshold and cost of learning. This "Human-in-the-Loop" learning approach is considered one of the most pragmatic paths for continuous improvement in current AI systems.

This loop means that every interaction you have with Hermes is never wasted. Even if it makes a mistake this time, that mistake itself becomes fuel for doing better next time. As usage time grows, Hermes becomes increasingly aligned with your personal work habits and project requirements, truly becoming an AI assistant that "understands you."

Summary and Reflections

Hermes represents an important direction in AI agent development: not pursuing ultimate performance in single invocations, but building capabilities for continuous learning and evolution. Under this approach, an agent's value continuously increases with usage time, creating genuine user stickiness and competitive moats.

For developers and AI practitioners, the design philosophy behind Hermes's Skills system and steering engineering is well worth studying. How to make AI systems improve during continuous operation while not losing control is a core challenge that every agent project must face.

Key Takeaways