Video: "Hermes Agent = AI SEO Machine!" by Julian Goldie on YouTube.
What "goal-based" means in practice
Most AI tools operate on a prompt-by-prompt basis: you ask, it answers, the session ends. Hermes Agent's goal mode works differently. You give the agent a target — "build a keyword strategy and content calendar for AI SEO automation" — and it decomposes that target into sub-tasks, works through them in sequence, and checks its own output against the original goal before calling the job done. If a step falls short, it retries rather than stopping.
The practical difference is that you can hand the agent a morning's worth of SEO work in one instruction and return to a set of completed files, rather than babysitting each stage. Julian Goldie's walkthrough shows this playing out on a real SEO brief: one input produces keyword clusters, a content plan, blog post drafts, and a suggested internal link structure — all without additional prompting in between.
The judge-style quality loop
The key mechanism that makes goal mode work reliably is the quality check. After completing a task, Hermes Agent runs a separate evaluation pass against the original brief. If the output does not meet the stated criteria — word count, coverage of the keyword cluster, structure of the content — the agent flags the shortfall and reruns the relevant section rather than marking it as complete.
This loop is what distinguishes the tool from a simple batch prompt. A batch prompt generates one output per input and moves on. The goal-mode loop keeps going until the output is acceptable, which means the quality threshold is set by the brief rather than by the model's confidence in its first attempt. In practice it means fewer corrections needed at the editorial review stage.
What the Kanban workflow looks like
Hermes Agent organises multi-step SEO work through a Kanban-style task board built into its Workspace interface. The orchestrator agent takes the initial brief and creates tasks on the board — one per sub-goal — then assigns each to the appropriate agent profile. Keyword research can run while competitor analysis runs in parallel; content drafting starts once research is complete; review runs after drafting finishes.
The Kanban layer is what makes parallel agent work manageable. Without it, you would need to manually sequence tasks and copy outputs between steps. With it, the coordination happens inside the agent system and you see the task progression in one dashboard. Julian Goldie's demonstration shows the board being populated and worked through over a single session with no manual task management.
What SEO tasks it handles
In the walkthrough, Hermes Agent covered: keyword cluster analysis, identifying primary and supporting terms for a topic set; content calendar planning with a publication schedule and topic distribution; individual blog post drafts with headings, subheadings, and body content matched to the keyword brief; and internal link recommendations based on the content structure already in the plan. Each of these is a task that would otherwise require a separate tool or a separate prompt sequence.
The limitation Julian Goldie notes is that the agent handles structured, repeatable SEO tasks well but still requires human oversight on editorial judgement — brand voice, genuinely original angle, content that competes on quality rather than volume. The loop is good at systematic execution; it is not a substitute for knowing what the content needs to say.
Where this connects to NordSys
We help UK businesses set up AI agent workflows for SEO — including Hermes Agent configurations that handle the systematic parts of content production so your team can focus on the parts that actually require judgement. If you want to understand whether a goal-based SEO agent is the right fit for your current workload, or you need help scoping the setup, get in touch and we can talk through what would work for your situation.
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