Building Agentic Workflows With Hermes Agent, Part 1

# Building Agentic Workflows With Hermes Agent, Part 1

## Why Start With Hermes Agent?

Software teams are moving past the question of whether large language models can help with engineering work. The more useful question now is: how do we build systems around them that are reliable enough to use?

A prompt in a chat window is useful. An API call to a model is useful. But neither is, by itself, an agentic workflow. Real workflows need context, tools, state, repeatability, boundaries, and observability. They need to survive ambiguity without becoming unpredictable. They need to connect model reasoning to actual systems: repositories, ticket trackers, documents, APIs, dashboards, terminals, browsers, and internal services.

That is where an agent harness becomes valuable.

This series is about building agentic workflows with Hermes Agent, an open-source agent framework from Nous Research. Hermes Agent provides scaffolding around model calls: tool use, an agent loop, memory, skills, multi-platform gateways, and subagents. In practical terms, it gives developers a place to define how an agent thinks, acts, remembers, delegates, and interacts with the outside world.

This first post explains why that harness matters.

## Models Are Not Workflows

A language model can produce a useful answer from a well-written prompt. But production workflows usually require more than one answer.

Consider a code review assistant. It may need to inspect a diff, understand the surrounding files, check whether tests cover the change, look for security issues, summarize risks, and leave comments in a review system. That is not a single model call. It is a sequence of decisions and actions.

Or consider an incident response assistant. It may need to read an alert, query logs, compare recent deployments, inspect runbooks, ask for confirmation before risky actions, and produce a timeline. Again, the model is only one part of the system.

The workflow needs a harness around the model.

Without one, teams often end up building the same plumbing repeatedly: tool adapters, retry logic, context assembly, state management, task decomposition, memory, permissions, and logging. These pieces are rarely glamorous, but they determine whether an agent is useful or fragile.

Hermes Agent is interesting because it treats that surrounding structure as a first-class concern.

## The Role Of An Agent Harness

An agent harness is the runtime and coordination layer that turns model reasoning into controlled action.

It does not replace the model. It gives the model a working environment.

A good harness answers questions like:

- What tools can the agent use?

- When should the agent call a tool instead of answering directly?

- How does the agent maintain context across steps?

- What should happen after a tool returns data?

- How are skills or reusable workflows defined?

- Can complex tasks be delegated to subagents?

- How does the same agent operate across different platforms?

- Where are boundaries enforced?

These questions matter because agentic systems tend to fail at the edges. The model may be capable, but the workflow breaks because it has too much context, too little context, poorly scoped tools, unclear stopping conditions, or no way to recover from partial progress.

Hermes Agent gives teams a way to design those edges deliberately.

## Tool Use Is Where Agents Become Useful

The simplest agentic pattern is: reason, choose a tool, observe the result, continue.

This loop is powerful because it lets the model work with live information instead of relying only on training data or the initial prompt. For software engineering workflows, tools might include file readers, search, test runners, linters, issue trackers, documentation systems, deployment APIs, or internal services.

But tool use needs discipline.

An agent with no tools is limited. An agent with too many tools is risky and often confused. A practical harness should make tool access explicit, structured, and inspectable. Engineers should be able to define what each tool does, what inputs it accepts, what it returns, and when it is appropriate to use.

Hermes Agent's tool-use model gives teams a foundation for controlled interaction. Instead of burying operational behavior in prompt text, you can expose capabilities as part of the agent runtime.

That distinction is important. Prompts are instructions. Tools are contracts.

## The Agent Loop Is The Core Abstraction

At the center of most agentic workflows is a loop:

1. Understand the current task and context.

2. Decide whether more information or action is needed.

3. Use a tool, call a skill, delegate, or respond.

4. Observe the result.

5. Continue until the task is complete or blocked.

This loop sounds simple, but it is where many production issues appear. Agents can overrun the task, call irrelevant tools, repeat themselves, lose track of goals, or stop too early. A harness gives developers a place to shape the loop: define stopping conditions, constrain actions, add checks, and make execution easier to inspect.

Hermes Agent is useful here because it gives the loop a home. The agent is not just a stateless completion endpoint. It is a running process with steps, observations, and decisions.

That makes workflows easier to reason about. It also makes them easier to improve.

When an agent fails, you want to know where it failed. Did it misunderstand the task? Did it choose the wrong tool? Did the tool return bad data? Did the agent ignore important context? Did it lack a skill that should have been reusable? A harness makes these questions answerable.

## Memory Turns Interactions Into Workflows

Memory is another reason to use an agent framework rather than raw model calls.

For a one-off answer, memory may not matter. For ongoing work, it matters a lot.

An engineering assistant may need to remember project conventions, previous decisions, user preferences, common workflows, or facts discovered earlier in a task. A leadership-facing assistant may need to preserve context across planning sessions, design reviews, and delivery updates.

The key is not simply "remember everything." That usually creates noise and risk. The useful pattern is selective memory: durable enough to reduce repetition, scoped enough to avoid polluting future tasks.

Hermes Agent's memory capabilities provide a path toward that balance. Memory becomes part of the workflow design rather than an accidental side effect of a long chat transcript.

## Skills Make Agents More Than Generalists

General-purpose agents are useful, but teams often need repeatable domain workflows.

A skill can encode a known procedure: triage a bug report, prepare a release note, investigate a flaky test, generate a migration plan, review an API change, or gather evidence for an operational alert. The model still reasons, but it does so inside a more specific playbook.

This is valuable for software teams because many high-value workflows are semi-structured. They require judgment, but they also have a known shape.

Hermes Agent's skill system gives teams a way to package that shape. Instead of relying on every prompt to restate the same process, teams can define reusable capabilities that agents can invoke when appropriate.

For technical leaders, this is one of the more important ideas. Agentic workflows should not live only in individual habits. They should become shared operational assets.

## Subagents Help With Complex Work

Some tasks are too broad for a single linear thread.

A planning agent might delegate research to one subagent, codebase exploration to another, and risk analysis to a third. A development workflow might separate test investigation, implementation planning, documentation, and review. A support workflow might divide log analysis, customer-impact assessment, and remediation options.

Subagents are not magic. They add coordination overhead, and they need clear boundaries. But when used carefully, they let workflows mirror how engineering teams already work: split the problem, gather focused results, then synthesize.

Hermes Agent's support for subagents makes this pattern available inside the harness. That matters because delegation should be structured, not improvised through prompt tricks.

## Multi-Platform Gateways Matter

Agents are only useful if they can meet teams where work happens.

For some workflows, that means a command-line interface. For others, it means chat, an IDE, a web app, a ticketing system, or a background automation. A good harness should not force every workflow into the same surface area.

Hermes Agent's multi-platform gateway approach is useful because it separates agent behavior from any single interface. The same underlying workflow can be exposed in different places, with platform-specific permissions and interaction patterns.

That is important for adoption. Engineers may want deep IDE integration. Operations teams may want chat-driven workflows. Leaders may want summarized reports. The harness should support those variations without requiring the core agent logic to be rewritten each time.

## Why Start With Hermes Agent?

Hermes Agent is a good fit for teams that want to build agentic systems deliberately rather than stitch together isolated model calls.

The value is not that it removes engineering work. The value is that it gives that work a clear structure.

You can define tools. You can shape the agent loop. You can add memory. You can package skills. You can delegate to subagents. You can expose workflows across platforms. Most importantly, you can treat the agent as a system that can be tested, inspected, improved, and governed.

That is the practical path for agentic workflows.

Not autonomous software engineers. Not magic coworkers. Just well-designed systems that combine model reasoning with explicit tools, reusable procedures, and operational boundaries.

In the rest of this series, we will move from concepts to implementation. We will look at how to design an agent loop, how to choose and constrain tools, how to write useful skills, how to use memory without creating a mess, and how to compose subagents into larger workflows.

Hermes Agent gives us the harness. The engineering challenge is learning how to use it well.