Most conversations about AI agents focus on the model. Which LLM. Which provider. Which benchmark. That is understandable — the model is the obvious variable — but it increasingly misses the point.
The harder problem is orchestration: how do you coordinate multiple agents, manage their context, give them tools, route tasks between them, and maintain coherent state across long-running workflows? That is where Hermes comes in.
What Hermes is
Hermes is a multi-agent orchestration framework. It is not a model. It is the layer that sits above models and coordinates how they work together — assigning tasks, passing context, managing agent personas, handling tool calls, and tracking what has happened across a session.
The core concept: you define agents with specific roles and capabilities, and Hermes manages the conversation between them and the outside world. Each agent can have its own system prompt, its own tool access, and its own scope of responsibility. The framework handles the plumbing.
Why it is getting attention
A few things make Hermes interesting in a crowded field:
It is model-agnostic. You can run it against whatever backend you prefer. This matters more than it sounds — locking into a single provider for orchestration logic is a compounding risk as the model landscape continues to shift.
The persona and context management is thoughtful. Keeping multiple agents coherent across a long session is genuinely hard. Hermes has clearly been designed by people who have hit the edges of that problem.
It stays close to how production systems actually work. Some orchestration frameworks are impressive demos. Hermes feels more like something you could build on.
My experience so far
I have been running Hermes in my own setup for a few weeks, primarily for research workflows and document processing tasks. My honest assessment: it is good, and it is getting better.
The rough edges are where you would expect them — complex multi-step workflows with many agents can get noisy, and debugging agent reasoning requires more tooling than currently exists. But the foundation is solid.
What I find most promising is the framing. Hermes treats agents as workers with defined roles, tools, and scope — which maps cleanly onto how you would think about designing a team of human specialists. That mental model makes it easier to reason about what an agent system is actually doing, which is essential for the governance work my research is focused on.
I will keep experimenting and report back as my thinking develops. For now: worth watching closely.
I am particularly interested in Hermes and similar frameworks from an enterprise architecture governance perspective — how do you audit, control, and govern agent ecosystems at scale? If you are thinking about this too, let’s talk.