Find local businesses without websites as leads
Hermes found mispriced supercars, generated plumber leads with pitch angles, and surfaced a Kimi K2 story most outlets missed. Task: Hermes found mispriced supercars, generated plumber leads with pitch angles, and surfaced a Kimi K2 story most outlets missed. The Hermes Agent Just Did 5 Things Most Agents Can’t — Here’s the Actual Output A single AI agent scraped a YouTube channel, identified content gaps, generated plumber leads with personalized pitch angles, flagged a mispriced supercar on Autotrader, and surfaced a Kimi K2 story that most tech outlets missed entirely. That’s 5 autonomous use cases — web scraping plus content gap analysis, cron job scheduling, lead generation for businesses without websites, price monitoring with deal alerts, and content ideation — all from one agent running on a CPU instance at $0.24 an hour. That’s not a pitch. That’s what happened in a live demo. The agent in question is Hermes, and if you’ve been watching the autonomous agent space, you’ve probably seen it mentioned alongside OpenClaw as an alternative. The comparison is worth making, but the more interesting story is what Hermes actually does when you point it at a real problem. The output is specific enough to be useful, and specific enough to evaluate honestly. Here’s what each use case looked like in practice. Use Case 1: Scraping a YouTube Channel and Finding What It Missed The prompt was simple: scrape the AI Grid YouTube channel, compare last week’s uploads to what was actually happening in the industry, and identify the gaps. Patient records✓ Granted Surgery schedule✓ Granted Billing export✕ Denied Hermes ran the scrape, processed the results, and returned a structured analysis. The observations were accurate. Four out of five videos uploaded that week were OpenAI-centric. One covered Anthropic. Zero covered certain days. The format skewed heavily toward tutorial and hype explainer content, with no deep research or model benchmark coverage. Then it listed the gaps. Specifically: the Claude Mythos preview, the Anthropic-Amazon deal, and Google Cloud Next. Three real stories that didn’t make the cut. This is the kind of thing that sounds trivial until you think about what’s actually happening. The agent isn’t just pulling a list of video titles. It’s comparing that list against a live picture of the industry, identifying the delta, and presenting it as a structured gap analysis. That’s a research workflow that would take a human analyst an hour to run manually, and it would probably be less thorough. The reason this works better in Hermes than in something like OpenClaw is that Hermes ships with web scraping and autonomous agent capabilities built in. With OpenClaw, you’d be installing separate tools to get to the same place — OpenClaw requires considerably more configuration to reach feature parity. Hermes has those skills out of the box. Use Case 2: Turning a One-Off Task Into a Recurring System After the content gap analysis ran, the follow-up prompt was: “Make this a recurring thing every Sunday at 9pm UK time.” Hermes set up the cron job. It flagged one caveat — the instance runs in the host’s local time zone, so the UK time would need to be hardcoded — and then confirmed the schedule. This matters more than it sounds. The difference between a task you run once and a task that runs every week without you touching it is the difference between a tool and a system. Cron scheduling is what turns Hermes from a capable assistant into something closer to infrastructure. The use case generalizes. If you’re tracking a topic — longevity research, regulatory filings, competitor pricing — you can set up the scrape once, schedule it, and have the results delivered to Telegram, Discord, or Slack on whatever cadence makes sense. You’re not checking manually. The agent is checking for you. For content creators specifically, this is a weekly competitive intelligence report that runs itself. For anyone else, it’s a monitoring system that doesn’t require a dedicated engineer to maintain. Use Case 3: Lead Generation With Pitch Angles Included The prompt here was direct: scrape the internet for Northwest London plumbing businesses, find leads that don’t have a website, and return three qualified leads so I can pitch them an AI-built website. Hermes spawned sub-agents per area. Each one went out, scraped, and returned results. The agent then ran a disqualification pass — filtering for leads with real addresses, a clear profile, and a natural pitch angle. It narrowed the list to three.; tools: Hermes Agent. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include access to Hermes Agent and web scraping capabilities. The agent may incorrectly assume absence from search results equals absence from the web (e.g., WhatsApp Business profiles). Human approval is strictly required before sending messages to avoid spam violations. Source reliability is secondary, based on a vendor blog post..
Tools
Published on Agent Work Atlas:
Overview
Hermes found mispriced supercars, generated plumber leads with pitch angles, and surfaced a Kimi K2 story most outlets missed.
Workflow
Hermes found mispriced supercars, generated plumber leads with pitch angles, and surfaced a Kimi K2 story most outlets missed. The Hermes Agent Just Did 5 Things Most Agents Can’t — Here’s the Actual Output A single AI agent scraped a YouTube channel, identified content gaps, generated plumber leads with personalized pitch angles, flagged a mispriced supercar on Autotrader, and surfaced a Kimi K2 story that most tech outlets missed entirely. That’s 5 autonomous use cases — web scraping plus content gap analysis, cron job scheduling, lead generation for businesses without websites, price monitoring with deal alerts, and content ideation — all from one agent running on a CPU instance at $0.24 an hour. That’s not a pitch. That’s what happened in a live demo. The agent in question is Hermes, and if you’ve been watching the autonomous agent space, you’ve probably seen it mentioned alongside OpenClaw as an alternative. The comparison is worth making, but the more interesting story is what Hermes actually does when you point it at a real problem. The output is specific enough to be useful, and specific enough to evaluate honestly. Here’s what each use case looked like in practice. Use Case 1: Scraping a YouTube Channel and Finding What It Missed The prompt was simple: scrape the AI Grid YouTube channel, compare last week’s uploads to what was actually happening in the industry, and identify the gaps. Patient records✓ Granted Surgery schedule✓ Granted Billing export✕ Denied Hermes ran the scrape, processed the results, and returned a structured analysis. The observations were accurate. Four out of five videos uploaded that week were OpenAI-centric. One covered Anthropic. Zero covered certain days. The format skewed heavily toward tutorial and hype explainer content, with no deep research or model benchmark coverage. Then it listed the gaps. Specifically: the Claude Mythos preview, the Anthropic-Amazon deal, and Google Cloud Next. Three real stories that didn’t make the cut. This is the kind of thing that sounds trivial until you think about what’s actually happening. The agent isn’t just pulling a list of video titles. It’s comparing that list against a live picture of the industry, identifying the delta, and presenting it as a structured gap analysis. That’s a research workflow that would take a human analyst an hour to run manually, and it would probably be less thorough. The reason this works better in Hermes than in something like OpenClaw is that Hermes ships with web scraping and autonomous agent capabilities built in. With OpenClaw, you’d be installing separate tools to get to the same place — OpenClaw requires considerably more configuration to reach feature parity. Hermes has those skills out of the box. Use Case 2: Turning a One-Off Task Into a Recurring System After the content gap analysis ran, the follow-up prompt was: “Make this a recurring thing every Sunday at 9pm UK time.” Hermes set up the cron job. It flagged one caveat — the instance runs in the host’s local time zone, so the UK time would need to be hardcoded — and then confirmed the schedule. This matters more than it sounds. The difference between a task you run once and a task that runs every week without you touching it is the difference between a tool and a system. Cron scheduling is what turns Hermes from a capable assistant into something closer to infrastructure. The use case generalizes. If you’re tracking a topic — longevity research, regulatory filings, competitor pricing — you can set up the scrape once, schedule it, and have the results delivered to Telegram, Discord, or Slack on whatever cadence makes sense. You’re not checking manually. The agent is checking for you. For content creators specifically, this is a weekly competitive intelligence report that runs itself. For anyone else, it’s a monitoring system that doesn’t require a dedicated engineer to maintain. Use Case 3: Lead Generation With Pitch Angles Included The prompt here was direct: scrape the internet for Northwest London plumbing businesses, find leads that don’t have a website, and return three qualified leads so I can pitch them an AI-built website. Hermes spawned sub-agents per area. Each one went out, scraped, and returned results. The agent then ran a disqualification pass — filtering for leads with real addresses, a clear profile, and a natural pitch angle. It narrowed the list to three.
Reported result
The source describes a live demo where the agent returned three qualified leads with specific pitch angles and unprompted legal caveats regarding outreach rules. No measured business outcomes, such as conversion rates or revenue, are reported. A measurable acceptance check would be verifying that at least 80% of generated leads have valid contact information and no existing website.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include access to Hermes Agent and web scraping capabilities. The agent may incorrectly assume absence from search results equals absence from the web (e.g., WhatsApp Business profiles). Human approval is strictly required before sending messages to avoid spam violations. Source reliability is secondary, based on a vendor blog post.
Try it
Act as a lead generation specialist using Hermes Agent. My goal is to find local businesses in [replaceable: target geography] within the [replaceable: industry niche] sector that do not have a website, to pitch them [replaceable: service, e.g., AI-built website]. Steps: 1. Configure Hermes Agent to scrape [replaceable: source URLs or search queries] for businesses in the target area. 2. Filter results to exclude any business with an active website URL. Ensure each lead has a valid physical address and phone number. 3. For each qualified lead, generate a personalized pitch angle based on their specific business type and location. 4. Compile a list of [replaceable: number] leads with their contact details, pitch angle, and a legal compliance check (e.g., verify no existing web presence via multiple sources). 5. Output the results in a CSV format with columns: Business Name, Address, Phone, Pitch Angle, Verification Status. Quality Measure: Manually verify the first 3 leads to ensure they truly lack websites and have valid contact info. Safeguards: Do not send any emails or messages. Present the CSV for human review and approval before any outreach. If scraping fails or yields no results, report the error and suggest alternative data sources. Ask clarifying questions if the target geography or niche is ambiguous.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.