This is a submission for the Hermes Agent Challenge.
Stop Treating AI Agents Like Chatbots: Hermes Agent as an SEO Indexing Operator
Most people still think about AI agents as smarter chatbots.
You open a chat window. You ask a question. The model answers. Maybe it writes some code. Maybe it summarizes a document. Maybe it gives you a checklist.
That is useful, but it is not the real promise of agents.
The real promise is not a chat window that gives better answers.
The real promise is a system that can own a messy recurring workflow, remember what happened last time, improve its process, and keep doing useful work without you explaining everything again from zero.
That is why Hermes Agent is interesting.
It is not only trying to be another coding assistant. It is closer to an always-on operator: something that can use tools, remember context, build reusable skills, run scheduled workflows, and connect to external systems through protocols like MCP.
To make that idea practical, I want to look at one boring but valuable workflow that many developers and indie builders ignore until it hurts:
website indexing operations.
Because launching a website is not the same as being discovered.
The problem: launching is not the same as being indexed
Every builder knows this feeling.
You ship a landing page. You write some docs. You publish a few blog posts. You submit the site somewhere. You wait.
Then nothing happens.
The pages exist, but search engines do not pick them up quickly. Some URLs are missing from the sitemap. Some pages have weak metadata. Some important pages are buried too deep. One page accidentally has a noindex tag. Another page points to the wrong canonical URL. The sitemap was generated before the latest deployment. The robots file is technically valid but blocks something important.
None of this feels dramatic.
That is the point.
Indexing problems are usually not one big obvious bug. They are small boring failures spread across your site:
- missing sitemap URLs
- stale sitemap entries
- accidental
noindextags - broken canonical tags
- weak page titles
- duplicate meta descriptions
- important pages with poor internal links
- orphan pages
- blocked routes in
robots.txt - changed URLs that were never resubmitted
- pages that exist in the app but not in the sitemap
- pages that are technically live but invisible to crawlers
A chatbot can tell you to check these things once.
But an agentic operator should be able to check them repeatedly, remember your site structure, notice recurring mistakes, and improve its process over time.
That is the difference.
A chatbot gives advice.
An operator owns the workflow.
Why Hermes Agent is a good fit for this kind of work
The interesting thing about Hermes Agent is not just that it can answer questions.
The interesting thing is the combination of features around persistent work:
- memory across sessions
- reusable skills
- tool use
- scheduled automations
- browser and web control
- MCP support
- sandboxed execution
- cross-platform access
- autonomous improvement loops
Those pieces are much more powerful when you stop thinking about agents as chatbots and start thinking about them as narrow operators.
A narrow operator does not need to do everything.
It just needs to do one workflow extremely well.
For example:
“Every time I deploy, audit my website for indexing readiness, identify blockers, suggest fixes, and prepare URLs that are safe to submit.”
That is a perfect agent workflow because it is repetitive, rule-heavy, tool-heavy, and easy for humans to forget.
It is also valuable because small SEO and indexing issues can quietly kill distribution.
The workflow: AI indexing operations
Imagine giving Hermes this responsibility:
“You are my website indexing operator. Your job is to keep my site discoverable, crawlable, and ready for search and AI discovery.”
The workflow could look like this:
- Crawl the website.
- Fetch
sitemap.xml. - Fetch
robots.txt. - Extract all discoverable URLs.
- Compare discovered URLs against sitemap URLs.
- Check HTTP status codes.
- Check canonical tags.
- Check robots meta tags.
- Detect accidental
noindexpages. - Check title and meta descriptions.
- Check Open Graph and Twitter card metadata.
- Check internal links.
- Detect orphan pages.
- Detect pages missing from the sitemap.
- Detect sitemap URLs that are dead, redirected, duplicated, or low-value.
- Detect pages that changed recently and should be submitted again.
- Submit safe URLs through supported indexing protocols.
- Generate a clear report.
- Create GitHub issues or PR suggestions for fixes.
- Remember recurring problems for the next audit.
This is not a flashy demo.
That is exactly why it is useful.
A lot of the best agent use cases are not cinematic. They are operational. They sit in the background and handle boring workflows that humans avoid.
Why a stateless chatbot is not enough
A normal chatbot can produce a checklist like this:
“Check your sitemap, robots.txt, canonical tags, meta tags, and internal linking.”
That is fine.
But next week, when your site changes, the chatbot does not know what happened last time. It does not remember that your blog lives under /blog. It does not remember that your docs are generated dynamically. It does not remember that your sitemap generator previously skipped a route group. It does not remember that you prefer reports in a specific format.
You have to re-explain the entire context again.
For one-off advice, that is acceptable.
For recurring operations, it is painful.
Hermes becomes more interesting because memory can turn a generic SEO checklist into a project-specific indexing process.
For example, after a few runs, the agent should know:
- the site’s main URL patterns
- which routes are important
- which routes should not be indexed
- where the sitemap is generated
- what recurring problems happen after deploys
- which URLs were already checked
- which reports were previously generated
- which fixes were already suggested
- what the developer prefers in the final report
That is where the agent starts to become useful.
Not because it is magical.
Because it remembers the boring details.
Memory turns audits into learning
The best part of using memory for indexing operations is that the agent can improve its judgment over time.
On day one, it might run a generic indexing audit.
On day three, it might remember that your /tools pages are programmatically generated and should always be checked against the sitemap.
On day five, it might remember that your canonical tags sometimes point to the production domain even in preview deployments.
On day seven, it might produce a shorter and more accurate report because it already knows which issues are expected and which issues are new.
That is the real value of an agent.
Not just doing a task once.
Doing the task repeatedly and becoming more adapted to the environment.
Skills turn repeated work into reusable procedure
A good agent workflow should not depend on writing a giant prompt every time.
That is where skills become useful.
For this use case, Hermes could have an indexing-audit skill.
Example skill design:
---
name: indexing-audit
description: Audit a website for crawlability, sitemap coverage, robots.txt issues, canonical mistakes, metadata gaps, and indexing readiness.
---
## Goal
Find everything that prevents a website from being discovered, crawled, indexed, or understood by search engines and AI search systems.
## Inputs
- website URL
- optional sitemap URL
- optional deployment URL
- optional GitHub repository
- optional list of priority pages
## Procedure
1. Fetch the homepage.
2. Fetch sitemap.xml.
3. Fetch robots.txt.
4. Crawl internal links from the homepage.
5. Extract all discoverable internal URLs.
6. Compare discovered URLs with sitemap URLs.
7. Check each important URL for:
- status code
- canonical URL
- robots meta tag
- title tag
- meta description
- heading structure
- Open Graph metadata
- internal links
8. Mark URLs as:
- ready to submit
- needs fixing before submit
- should not be indexed
- unknown / needs manual review
9. Generate a markdown report.
10. Save recurring observations to memory.
11. Suggest one improvement to this skill.
## Output Format
# Indexing Readiness Report
## Summary
## Critical Blockers
## Quick Fixes
## Sitemap Issues
## Robots and Noindex Issues
## Canonical Issues
## Metadata Issues
## Internal Linking Issues
## URLs Ready to Submit
## URLs Not Safe to Submit
## Suggested GitHub Issues
## What I Should Remember For Next Time
This is where the workflow starts becoming reusable.
Instead of asking Hermes to “do SEO” in a vague way, you give it a precise operating procedure.
Then the agent can improve that procedure when it finds new recurring patterns.
MCP makes the workflow extensible
Indexing operations need tools.
The agent might need to:
- crawl a site
- parse a sitemap
- check a robots file
- inspect page HTML
- read a repository
- open a GitHub issue
- create a pull request
- call an indexing API
- store a report
- compare results against the previous run
This is where MCP becomes important.
Instead of hardcoding every possible integration into the agent, MCP lets the agent connect with external tools and systems in a cleaner way.
For an indexing operator, possible MCP-connected tools could include:
- a GitHub MCP server for issues and PRs
- a filesystem MCP server for reading generated files
- a custom crawler tool
- a sitemap parser
- an IndexNow submission tool
- a database of submitted URLs
- a Search Console-style data source
- an analytics source
- a deployment provider integration
The point is not that one agent should do everything.
The point is that the agent can sit above the workflow and coordinate the right tools.
Scheduling turns the agent into infrastructure
A manual audit is helpful once.
A scheduled audit is infrastructure.
Indexing checks should run:
- after every deployment
- after publishing new content
- after editing important pages
- after changing routes
- after a sitemap update
- after a domain migration
- every morning for active sites
- weekly for stable sites
This matters because indexing problems often happen after normal development work.
A route changes. A build script changes. A new content collection is added. A canonical base URL is misconfigured. A preview setting leaks into production.
Nobody remembers to check everything manually.
An agent can.
A Hermes automation could run an indexing audit every morning and produce a report like this:
Daily Indexing Report
Site: example.com
Date: 2026-05-24
Summary:
- 43 URLs checked
- 39 URLs healthy
- 2 URLs missing from sitemap
- 1 accidental noindex detected
- 1 redirected sitemap URL found
Critical:
- /pricing has noindex but appears to be a public conversion page
Quick fixes:
- Add /blog/new-launch-post to sitemap
- Remove redirected /old-features URL from sitemap
- Confirm canonical on /tools/meta-generator
Ready to submit:
- /blog/new-launch-post
- /tools/meta-generator
Remember:
- The /tools route group should always be included in sitemap checks.
That is the kind of output a developer can act on quickly.
A practical architecture
Here is a simple architecture for Hermes as an SEO indexing operator:
Website / Repository
↓
Hermes Agent
↓
Indexing Audit Skill
↓
Tool Layer
- crawler
- sitemap parser
- robots checker
- metadata checker
- canonical checker
- IndexNow submitter
- GitHub issue creator
↓
Outputs
- indexing-readiness.md
- critical blocker list
- submit-ready URL list
- suggested fixes
- GitHub issues
- memory updates
The important part is separation of responsibility.
Hermes should not be treated as one giant magic function.
It should coordinate a workflow:
- tools gather facts
- the skill defines the procedure
- memory stores project-specific learning
- scheduling makes the workflow repeat
- reports make the result human-readable
That is a much healthier way to build with agents.
Example prompt for Hermes
Here is the kind of prompt I would use:
Use the indexing-audit skill on my website.
Website: https://example.com
Goal:
Find everything preventing this site from being discovered, crawled, indexed, or understood by search engines and AI search systems.
Check:
- sitemap.xml
- robots.txt
- canonical tags
- noindex tags
- broken internal links
- missing metadata
- duplicate titles
- duplicate meta descriptions
- weak descriptions
- orphan pages
- pages missing from sitemap
- sitemap URLs that redirect or 404
- recently changed URLs that should be submitted through supported indexing protocols
Output:
1. Critical blockers
2. Quick fixes
3. Sitemap issues
4. Robots/noindex issues
5. Canonical issues
6. Metadata issues
7. Internal linking issues
8. URLs ready to submit
9. URLs not safe to submit
10. Suggested GitHub issues or PR changes
11. What you should remember for the next audit
12. One improvement to the indexing-audit skill
This prompt is intentionally specific.
Agents work better when the workflow has clear boundaries.
“Improve my SEO” is vague.
“Run an indexing readiness audit and classify URLs by submit safety” is operational.
What the agent should not do automatically
A good indexing operator should also know when to stop.
It should not blindly submit every URL.
Some URLs should not be indexed:
- admin pages
- auth pages
- duplicate pages
- thin pages
- test pages
- filtered parameter pages
- private dashboards
- pages with intentional
noindex - pages blocked for a reason
This is important because automation without judgment creates spammy behavior.
The agent should classify URLs before submission:
Ready to submit:
- public, canonical, indexable, useful pages
Needs fixing first:
- public pages with metadata/canonical/sitemap problems
Do not submit:
- private, duplicate, blocked, thin, or intentionally noindex pages
Needs manual review:
- ambiguous pages or pages with conflicting signals
This makes the workflow safer.
The goal is not to spam indexing endpoints.
The goal is to keep the site technically clean and submit only the right URLs.
Where this gets especially useful for indie hackers
Indie hackers usually do not have a dedicated SEO engineer.
They launch quickly. They pivot quickly. They change landing pages quickly. They add tools, blog posts, changelog pages, docs, directories, and comparison pages.
That speed creates indexing drift.
Your website changes faster than your search hygiene.
An indexing operator could become part of the launch process:
- Build the page.
- Deploy it.
- Hermes audits it.
- Hermes finds indexing blockers.
- Hermes suggests fixes.
- Hermes opens issues or PR suggestions.
- Hermes prepares safe URLs for submission.
- Hermes remembers what went wrong.
This is much more useful than asking a chatbot for general SEO advice.
It becomes an actual workflow.
A seven-day test I would run
To prove whether Hermes is actually useful for this, I would test it on the same website for seven days.
The goal would not be to see whether it can do one audit.
The goal would be to see whether it improves.
Day 1: Generic audit
Run the indexing audit with a basic checklist.
Expected output:
- sitemap issues
- metadata issues
- robots/noindex checks
- internal linking suggestions
Day 3: Project-specific memory
Run the audit again after adding or changing pages.
Expected improvement:
- remembers important route groups
- compares against the previous report
- identifies new issues instead of repeating old ones blindly
Day 5: Skill refinement
Let Hermes suggest improvements to the indexing-audit skill.
Expected improvement:
- adds checks for recurring site-specific problems
- removes noisy checks that are not useful
- improves the report format
Day 7: Operational report
Run the final audit.
Expected improvement:
- shorter report
- clearer prioritization
- fewer false positives
- better URL classification
- stronger memory of site conventions
That would be the real proof.
Not whether the agent can produce a checklist.
Whether the agent becomes better at this website over time.
The bigger lesson: agents should own workflows, not conversations
The more I think about Hermes, the more I believe the best agent use cases will not look like chatbots.
They will look like small operators.
A dependency upgrade operator.
A pull request review operator.
A release notes operator.
A support triage operator.
A docs freshness operator.
A website indexing operator.
Each one has a clear job.
Each one has memory.
Each one has tools.
Each one has a repeatable process.
Each one gets better through usage.
That is a much more useful mental model than “AI assistant that can do anything.”
Because “do anything” often becomes “do nothing reliably.”
A narrow operator can be judged.
Did it find the indexing issue?
Did it avoid submitting the wrong URLs?
Did it remember the route structure?
Did it produce a better report than last time?
Did it reduce manual work?
Those are concrete outcomes.
Final thought
The most underrated developer workflows are not hard because they are intellectually complex.
They are hard because they are repetitive, easy to forget, and full of small edge cases.
That is exactly where self-improving agents should live.
Not as chatbots waiting for another prompt.
As operators that remember, repeat, verify, and improve.
For me, Hermes Agent is exciting because it points toward that kind of future.
A future where agents do not just answer questions.
They own workflows.
And for a small builder trying to get a website discovered, an always-on indexing operator might be far more valuable than another generic AI chat window.
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