Hermes Agent Deep Dive: Anatomy of a Self-Evolving AI Agent Architecture

Why Is Hermes More Popular Than Claude Code?

Recently in the AI coding community, Hermes Agent has surpassed Claude Code in popularity. The reason is simple—both write code, but Hermes gets smarter the more you use it, while Claude Code can't do that.

This isn't magic; it's a fundamental difference in engineering design. Hermes achieves one critical thing: every time you complete a task with it, it automatically stores the experience from that session. The next time it encounters a similar task, it picks up right where it left off using previously saved experience.

This experience accumulation mechanism is essentially a combination of persistent In-Context Learning and Retrieval-Augmented Generation (RAG). Traditional large language models lose all context information after each conversation ends. Even advanced coding assistants like Claude Code, while supporting project-level CLAUDE.md memory files, still fundamentally rely on users to maintain them manually. Hermes's approach is closer to the concept of "procedural memory" in cognitive science—solidifying repeatedly executed operational patterns into reusable skill units, similar to the human transformation from "deliberate practice" to "muscle memory."

Based on real-world testing data, by the 14th time performing the same task, completion speed improves by over 60%, with accuracy still climbing. This is the practical effect of "self-evolution"—not starting from scratch every time, but continuously iterating on top of previous experience.

Hermes Agent's Two Core Engineering Architectures

Many people assume Hermes is inherently smart, but that's not the case. The reason it gets stronger with use comes down to two carefully designed engineering systems behind the scenes.

Skills System: Enabling the Agent to Autonomously Learn New Capabilities

The first system is called Skills, responsible for loading new capabilities into Hermes. The most powerful aspect of this system is that it can complete iterations on its own without your involvement.

Specifically, after Hermes completes a task, it automatically analyzes the methods used, pitfalls encountered, and final solutions, then distills these into a "skill" for storage. The next time a similar scenario arises, this skill is automatically invoked.

The Skills system's design philosophy originates from the concept of "Policy Distillation" in reinforcement learning, but with a much lighter implementation. In reinforcement learning, agents optimize strategies through reward signals obtained by interacting with the environment; Hermes's Skills system instead compresses the complete path of each successfully completed task—including decision chains, tool invocation sequences, and error handling approaches—into a reusable skill template. The difference from early autonomous agents like AutoGPT is that AutoGPT plans from scratch every execution, while Hermes dramatically reduces redundant planning overhead through skill reuse. From a software engineering perspective, this is analogous to compiling "runtime experience" into "precompiled modules," achieving an efficiency leap from interpreted to compiled execution.

This means you don't need to manually write prompt templates or maintain knowledge bases—Hermes learns and grows from real-world practice on its own.

Governance Engineering (GPA Mechanism): Preventing the Agent from Getting Dumber as It Grows

The second system is called Governance Engineering (GPA Mechanism), responsible for keeping Skills well-managed and properly utilized.

This mechanism solves a critical problem: the more capabilities an agent accumulates, the easier it is for it to "get dumber as it grows." Skills may conflict with each other, invocation order may become chaotic, and output quality ultimately degrades.

This problem is known in AI systems as "performance degradation from capability bloat," and it's prevalent in traditional software engineering as well, manifesting as systems becoming bloated and unstable as features increase. In the AI agent domain, this problem is even more severe: when too many skills accumulate, the model experiences decision difficulty when choosing which skill to invoke, similar to the human "paradox of choice." The GPA mechanism is essentially a Meta-Controller—it doesn't execute tasks directly but handles skill version management, conflict detection, priority ranking, and deprecation cleanup.

The GPA mechanism's role is to effectively manage and orchestrate all skills. Even more noteworthy, GPA can not only modify Skills but also reverse-modify your system prompts. In other words, it automatically optimizes the entire agent's behavioral framework based on actual operational results. This design that can modify system prompts means it possesses "self-restructuring" capability—a fairly cutting-edge design pattern in AI Agent architecture, approaching an engineering implementation of "Adaptive System Prompting."

Skills + Governance Engineering—with both pieces in place, the agent won't get dumber as it grows. This is the most core principle in Hermes's design philosophy.

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

Hermes comes with 47 built-in tools covering common scenarios like file operations, code execution, network requests, and data processing. Compared to Claude Code's approach of relying on external toolchains, Hermes integrates these capabilities directly internally, reducing uncertainty in the invocation chain.

From an architectural perspective, Claude Code adopts a "Tool Use/Function Calling" architecture, connecting to external tool services through protocols like MCP (Model Context Protocol). This architecture's advantage is high flexibility and an open ecosystem, but the cost is that every tool invocation involves network requests, serialization/deserialization, error retries, and other overhead. Hermes's strategy of embedding 47 tools internally is closer to the Unix philosophy of "self-contained" design. From a Site Reliability Engineering (SRE) perspective, each additional external dependency reduces overall system availability multiplicatively—if each external API has 99.9% availability, chaining 10 together leaves only 99%. Built-in tools completely eliminate these uncertainties, with the trade-off being that updates require a full upgrade. This is a classic engineering trade-off between "coupling vs. reliability."

This brings two direct benefits:

• Higher stability: Tool invocations don't depend on external API availability, reducing task failures caused by third-party service fluctuations
• Faster response: Built-in tool invocation latency is far lower than external API calls, improving overall task execution efficiency

Hermes Practical Guide: From Local Installation to Feishu Integration

Local Installation and Deployment Steps

Hermes supports direct installation and execution on local machines. For users with zero background, the entire deployment process isn't complicated—following the official documentation steps is sufficient to complete the basic environment setup. The key is ensuring your local environment meets basic runtime requirements, including a Python environment and necessary dependency libraries.

Feishu Integration: Commanding AI from Your Phone

A more practical approach is connecting Hermes to Feishu (Lark). Once connected, you can directly command Hermes through Feishu messages on your phone—whether it's writing code, processing data, or executing automation tasks, you can initiate them anytime, anywhere.

Integrating AI Agents into instant messaging platforms is one of the mainstream paradigms for Agent deployment today, known in the industry as a "Conversational Agent Interface." The core value of this pattern lies in embedding AI capabilities into users' existing workflows rather than requiring them to switch to a new tool interface. Technically, this typically involves Webhook listeners, message queues, asynchronous task execution, and other backend architecture. Feishu's open platform provides Bot APIs and event subscription mechanisms, through which Hermes implements a complete closed loop of message reception, task execution, and result delivery. This "phone remote control" mode effectively deploys the Agent as a resident background service (Daemon), standing by 24/7 to respond to commands.

This "phone-controlled AI work" model dramatically lowers the barrier to entry and transforms Hermes from a development tool into a personal assistant always at your side.

Establishing Error Correction Feedback Loops for Continuous Growth

The most critical step is establishing an error correction feedback loop. When Hermes's output doesn't meet expectations, you simply point out the error and provide the correct direction, and Hermes records this correction experience through the Skills system.

Hermes's error correction feedback loop is similar in principle to RLHF (Reinforcement Learning from Human Feedback) used in large model training, but operates at a completely different level. RLHF acts on model weights, requiring large amounts of labeled data and GPU compute to fine-tune model parameters; Hermes's correction mechanism operates at the "experience layer"—it doesn't change the underlying model's weights but adjusts behavior by modifying stored skill templates. This is closer to "automated iteration of prompt engineering." From a user experience perspective, this design's advantage is that feedback takes effect immediately, requires no retraining, and each user's correction experience is isolated, forming a personalized capability profile.

As corrections accumulate, Hermes's understanding of your work habits, coding style, and business logic deepens. This is the underlying logic of "getting smarter the more you use it"—not through a larger model, but through continuously accumulated personalized experience. It's essentially building a dedicated skill library and behavioral preference model for each user.

Conclusion: Self-Evolution Is the Right Direction for AI Agents

Hermes demonstrates an important trend: future AI tool competition won't be about how smart a single conversation is, but about whether it can continuously accumulate and evolve.

A self-learning, self-iterating agent becomes more valuable the longer you use it. This stands in stark contrast to traditional "use-and-discard" AI tools. The Skills system handles capability accumulation, Governance Engineering handles capability management, and 47 built-in tools provide a stable execution foundation—all three working together form a complete self-evolving agent architecture.

For developers looking to dive deeper into practice, Hermes is well worth serious study and hands-on experience.