Installing an AI agent is not the finish line. The useful moment is when the agent can perform a repeatable workflow with clear boundaries and evidence that it actually worked.
This article is the companion to Minte AI Academy Episode 2 and follows the same order as the video:
- verify the install
- make one normal chat work
- add reusable knowledge with a skill
- isolate a purpose with a profile
- schedule a harmless recurring task
The order matters. Hermes' official quickstart makes the same recommendation: get one clean conversation working before layering on gateways, cron, skills, voice, or routing.
1. Verify what is actually installed
On Windows, macOS, Linux, or WSL, begin with the executable itself:
hermes --version
hermes config path
hermes config check
The version command tells you which binary answered. config path identifies the active Hermes home, and config check catches missing or stale configuration. If a provider has not been configured yet, use hermes setup or hermes model before troubleshooting anything more complicated.
A green install is not proof that an agent can answer. It only proves that the command is available.
2. Prove one real chat
Run a small, boring request:
hermes chat -q "Reply with exactly three words: Hermes is working"
The exact prompt does not matter. The proof does. You want an actual model response, not just a running process, an open port, or a saved API key. If the request fails, stay at this layer. Check the selected provider and model, authentication, base URL, and context length. hermes doctor is useful here:
hermes doctor
Do not add a gateway or cron job while the smallest loop is broken. Every higher-level feature makes the failure harder to locate.
3. Turn a repeated procedure into a skill
A Hermes skill is reusable operating knowledge. It is the place for the rules you would otherwise paste into every session. A focused skill normally contains:
- when it should trigger
- inputs and assumptions
- numbered steps
- pitfalls
- verification criteria
Inspect what is available locally with:
hermes skills list
hermes skills search "research"
For a local skill, keep the instructions narrow. A skill should shape a class of work, not become a second operating system. Skills also do not replace judgment: they provide a proven procedure, while the agent still has to inspect the current source and verify the result.
The episode-production workflow used for this article is itself a good example. It says that a finished video needs real evidence, a raw source, a cleaned master, a manifest, and independent media checksβnot just a success message from a renderer.
4. Use profiles as safety boundaries
Sessions are conversations. Profiles are independent Hermes homes. The profiles documentation describes separate configuration, environment, memory, sessions, skills, cron jobs, and state databases. That makes profiles useful when one machine hosts several distinct purposes.
Create a research profile like this:
hermes profile create research
research setup
research chat
Use a descriptive name and decide what belongs inside it before adding credentials or automation. A research profile can have a different model, skills, memory, and cron surface than a coding profile. Keep credentials per profile, and do not copy private .env values into a public tutorial.
The important distinction is isolation: changing a research profile should not silently change the behavior of your main coding profile.
5. Schedule a safe first task
Hermes cron supports one-shot and recurring jobs, pause, resume, editing, triggering, removal, attached skills, local delivery, platform delivery, and no-agent scripts. Start with a local artifact rather than a public message or a money-moving action. For example, a daily research brief can write three bullets to a local file.
Conceptually, the job should be constrained like this:
Schedule: every weekday at 09:00
Delivery: local
Task: write three bullets to a known file
Verification: inspect the latest run and the file
For unattended language-model work, pin the provider and model when you need predictable behavior. Hermes protects against accidental configuration drift: if an unpinned job detects a provider or model change, it can fail closed instead of silently inheriting a new paid route. That is a feature, not a nuisance.
The verification step is part of the job. A scheduler row is not proof that the task produced the expected artifact. Read the latest output and inspect the file.
The operating rule
The whole workflow is simple:
Make the smallest real loop work, then add one layer at a time.
Chat before gateway. Reusable skills before copy-paste prompts. Profiles before mixing unrelated jobs. Automation only after the underlying action is verified.
Official references
The video uses privacy-safe terminal visualizations based on verified local CLI output. Credentials, private IDs, and delivery targets are intentionally omitted.
Build it with the video, then keep the package as your starting runbook for the next Hermes workflow.