Learn Hermes AI Agent - History, Background & Why Choose Hermes AI Agent

#Introduction

In episode 0 you prepared the foundational skills and tooling: Node.js, VS Code, the Hermes CLI, LLM provider credentials, and Docker for sandboxing. Now it is time to understand why we go to the trouble of building an agent — rather than just calling the model directly.

Here is the roadmap for this episode: we will trace how AI agents have taken on a role in modern applications, compare agent architecture with model-only workflows, and then break down the three main strengths of Hermes AI Agent — event-driven, plugin extensibility, and orchestration — along with when you should choose Hermes and when you should not.

#The Role of AI Agents in Modern Applications

Ten years ago, an "AI application" usually meant a chatbot that answered FAQs using if-else rules. Five years ago, it became an LLM API wrapper: prompt in, text out. Both are reactive — waiting for input, producing output, then stopping.

Modern applications demand more. Applications need systems that can:

• Take action, not just talk: send emails, update databases, trigger build pipelines.
• Use tools: search the web, read files, execute code, query internal APIs.
• Contribute to workflows, either as a human assistant or as an autonomous worker in automated pipelines.

This is the role that AI agents fill. They are no longer a chat widget pinned to the corner of an application, but a processing component on par with a backend service — with one advantage: they can decide which tool to use to complete a task. In companies, agents handle support tickets, run scheduled reports, moderate content, and analyze conversations. All of it without handwritten branching code.

#Agent Architecture vs Model-Only Workflows

#Model-Only Workflows

The simplest pattern is model-only: one request to the LLM, one text answer. All the surrounding logic is written manually in the application code.

async function answerQuestion(question: string): Promise<string> {
  const response = await llmClient.chat({
    messages: [
      { role: "system", content: "Kamu adalah asisten yang ringkas." },
      { role: "user", content: question },
    ],
  });
  return response.text;
}

Notice the limitations. If the question is "what is the stock level of product X?", this workflow must first know how to call the stock endpoint, then inject the result into the prompt. Every variation of the question needs a new code branch. The model never "makes a decision" — all decisions rest with the developer.

#Agent Architecture

In agent architecture, the model is given a list of tools and the freedom to choose when to call them. The model returns a tool call; the runtime executes it; the result is returned to the model; the loop continues until a final answer.

const agent = new HermesAgent({ model: "gpt-4o" });
 
agent.registerTool(getStockLevel);
agent.registerTool(getProductInfo);
 
const answer = await agent.run(
  "Berapa stok produk SKU-123?",
);

The difference is fundamental: the code above does not dictate the sequence of steps. The agent decides, from the conversational context, that checking stock requires calling getStockLevel — and then assembles the answer from the result. Correctness is determined by the model, not by the developer. That is why agent evaluation and testing (episodes 13 and 19) has become its own serious discipline.

#The Strengths of Hermes AI Agent

#Event-Driven

Hermes is built as an event-driven runtime. The execution flow is not rigid linear code but a sequence of events: message received, turn started, tool called, tool finished, message sent. Each of these points emits an event that your application can hook into.

hermes run --profile support --events

With event-driven architecture, you can observe every agent step in real time, inject logic in the middle of execution (for example, rejecting a dangerous tool), or forward events to an observability pipeline. This is what sets Hermes apart from other agent SDKs that treat execution as a monolithic function.

#Plugin Extensibility

Hermes is modular: the kernel provides core services, and almost everything else — tools, memory providers, the context engine, even messenger platforms — is a plugin. You do not need to fork the repository or rewrite the runtime to add capability.

• Tool plugins add new capabilities through a single function registered in the registry.
• Memory providers swap how long-term memory is stored.
• Platform adapters connect the agent to Telegram, Discord, Slack, or WhatsApp.

With plugins, teams can share modules across projects without copying code — we will write our first plugin in episode 16.

#Orchestration

The third strength is orchestration: Hermes can manage work far more complex than a single conversation. A cron scheduler runs scheduled tasks; subagents are delegated for parallel work; multiple agents coordinate to achieve one big goal. All of it is controlled by the same kernel as a single agent — there is no separate runtime.

This matters in practice: an agent you build today for simple chat can grow into a multi-agent orchestrator without changing the foundation. Episodes 10, 14, and 17 will deepen each of these patterns.

#When to Choose Hermes AI Agent

Honestly, not every problem needs an agent. Some practical guidelines:

• Use model-only if the task is deterministic, does not need tools, and can be written with a few code branches.
• Use an agent if the task needs dynamic decisions: choosing tools, exploring, or adapting to changing context.
• Choose Hermes if you need three things at once: control through events, extensibility through plugins, and orchestration for both scheduled and multi-agent work.
• Consider alternatives if you only need a chat wrapper inside an existing framework — in episode 22 we will compare Hermes with other ecosystems.

#Conclusion

This episode placed Hermes AI Agent in context: the role of AI agents in modern applications that no longer just talk but act; the fundamental difference between agent architecture and model-only workflows; and Hermes's three strengths — event-driven, plugin extensibility, and orchestration.

Key takeaways:

• AI agents move the decision about which tool to use from developer code to the model runtime.
• Model-only suits deterministic answers; agents suit dynamic decisions.
• Event-driven architecture gives you a control point at every step of agent execution.
• The plugin system makes Hermes extensible without modifying the kernel.
• Orchestration lets a single runtime handle cron jobs, subagents, and multi-agent setups.

In the next episode 2 we will take apart the core concepts and main architecture of Hermes AI Agent — the controller, kernel, tools, memory, and environment components; the agent lifecycle from perception, planning, and action to reflection; as well as LLM integration, tool invocation, and custom actions. Brace yourself, because this is the most technical episode of the early phase!