A task arrives with project context and an intended outcome.
Agent workflow
How I structured my AI agent workflow
Hermes is where I started treating AI less like one chat box and more like a workflow system: profiles for different roles, memory for durable context, skills for reusable procedures, and review gates for judgment-sensitive work.
Profiles
Profiles turn AI work into assigned roles
The most useful Hermes feature to explain publicly is profiles. They make the workflow legible: different profiles are configured for different kinds of work instead of forcing every task through the same model, prompt, and context.
Interprets the work, selects the lane, and preserves the review boundary.
Decomposition, QA, final judgment, and review gates.
Design polish, layout critique, hierarchy, and accessibility review.
Lower-cost inventory building, source clustering, and research passes.
Implementation experiments, alternate hosted models, and local model serving tests.
| Profile | Role in the workflow | Why it matters |
|---|---|---|
default |
Front door, context, and task routing | Keeps direct requests grounded in project context and sends specialized work to the right lane. |
orchestrator |
Planning, decomposition, review, and QA | Separates judgment and acceptance checks from raw implementation. |
coding |
Primary implementation lane | Provides the strong default path for building, testing, and repairing code. |
design |
UI/UX critique and polish | Gives interface work a dedicated visual and accessibility review lane. |
cybersecurity |
Security research and risk review | Keeps security-sensitive investigation, threat analysis, and remediation advice in a dedicated lane. |
knowledge-worker |
Research, source inventory, and documentation | Uses an economical lane when collecting and organizing evidence is the job. |
baseten |
Controlled low-cost draft lane | Useful for bounded first passes; the orchestrator still gates the result before it has impact. |
marketing-seo |
Marketing, content, and SEO work | Gives audience research, content planning, and search-oriented work a dedicated lane. |
inbox-intake |
Inbox triage and background intake | Keeps recurring intake work separate from the interactive project workflow. |
omlx-coder |
Local-model experiment lane | Retains a clear place to evaluate privacy, cost, latency, and serving constraints without making it the default. |
Memory + skills
Making AI work durable instead of disposable
The goal is continuity. I do not want every AI session to restart from scratch, and I do not want repeated procedures to live only in prompt history.
Durable project context
Persistent memory and Obsidian-backed notes preserve decisions, source evidence, project conventions, and review constraints across sessions. That turns AI from a one-off assistant into a continuity layer for project work.
Reusable procedures
Hermes skills act like workflow playbooks: project planning, debugging, browser automation, deployment, testing discipline, and design review. When a pattern works, it can be captured and reused instead of reinvented.
Model routing + review
The right lane depends on the job
One of the biggest lessons has been that the answer is not always “use the strongest model.” The right choice depends on cost, quality, privacy, risk, and whether the work needs reasoning at all.
Takeaway
What this taught me
Enterprise AI is workflow design. Models matter, but so do state, review, logging, access control, repeatable procedures, and knowing when not to use a model at all.