Lead generation gets messy fast.
You start with a clear goal, like “find SaaS companies hiring DevOps engineers” or “find B2B teams that probably need automation help,” and five minutes later you are buried in tabs, random LinkedIn pages, weak-fit companies, and notes that never make it into a clean pipeline.
That was the problem I wanted to fix.
Instead of treating lead gen like a giant scraping problem, I used Hermes Agent as a research and qualification layer. The goal was not to replace judgment. The goal was to make the boring parts faster, more consistent, and less chaotic.
So the workflow became simple:
- define the ideal customer profile
- search for candidate companies and contacts
- enrich them with context from the web
- qualify them with a clear rule set
- ship only the better leads into a clean output
That is the part I care about most. Not “AI found leads.”
Better leads. Better reasons. Less junk.
What the system does
At a high level, the Hermes lead-generation workflow acts like a research assistant with tools.
It can:
- search the web for companies and people
- read company pages and public context
- keep track of qualification patterns
- score leads against an ICP
- return structured output for a sheet, CRM, or manual review
That makes it more useful than a one-shot prompt.
A single prompt can produce a list.
A tool-using agent can produce a workflow.
Architecture diagram
The flow is intentionally boring in a good way:
- collect possible leads from public sources
- apply ICP filters early
- let Hermes research and enrich each candidate
- score for fit and confidence
- keep a human review step for edge cases
- export CRM-ready rows with reasons attached
That last part matters a lot.
A lead score without a reason is not that helpful. If the system says a company is a strong prospect, I want to know why it thinks that:
- company size matches
- role matches
- hiring signal exists
- public stack suggests a likely use case
- timing or pain signal is visible
Now the output becomes usable instead of decorative.
Step 1: Start with a tight ICP
This is where most lead-gen workflows quietly fall apart.
If the ICP is vague, the output will be vague too.
“Find companies that may need automation” is too broad.
A better version looks like this:
- B2B SaaS companies
- 20 to 300 employees
- hiring in engineering, DevOps, or platform roles
- active product or infrastructure complexity
- likely need for automation, AI ops, or workflow cleanup
That is enough for Hermes to work with.
It does not need a perfect enterprise sales playbook on day one. It just needs a clear lane.
Step 2: Use Hermes for research, not just list-making
This is the real shift.
Most lead-gen automations stop at extraction. They pull names, titles, and URLs, then dump everything into a spreadsheet.
Hermes can go one step further.
It can search, fetch, compare, and summarize context around a company or person before deciding whether the lead is worth keeping.
For example, instead of just returning a company name, the agent can look for signals like:
- hiring pages that mention platform, infra, data, or automation work
- product complexity visible from docs or engineering blogs
- company stage and team size
- whether the role you care about even exists there
- whether the business fits the market segment you want
That turns raw prospects into candidates with context.
Step 3: Add qualification logic before outreach
This is the part that saves the most time.
I do not want every found lead to count equally.
I want a lightweight qualification layer that answers questions like:
- Is this even the right kind of company?
- Does this contact look relevant?
- Is there any visible signal that they may need the solution?
- Is the confidence high enough to keep, or does it need review?
A practical way to do this is to combine:
- ICP fit for company-level filtering
- role fit for contact-level filtering
- pain or timing signals from public data
- simple qualification rules inspired by frameworks like BANT
I am not trying to force a full enterprise sales methodology into a first-pass research agent.
I am using a lightweight version of it to reduce obvious bad fits.
A sample output object can look like this:
{
"company": "ExampleCloud",
"contact": "Jane Doe",
"role": "Platform Engineering Manager",
"industry_fit": true,
"size_fit": true,
"signal": "Hiring for SRE and DevOps roles",
"score": 84,
"confidence": "high",
"reason": [
"matches B2B SaaS ICP",
"engineering hiring signal present",
"contact role aligns with likely buyer/user"
],
"review_required": false
}
That is a way better handoff than a flat CSV with 60 names and zero context.
Qualification loop diagram
The loop matters because this system gets better when you review misses.
A few examples:
- some companies match the keywords but are too early-stage to matter
- some contacts have senior titles but are not the right function
- some pages look relevant but the need is weak once you read deeper
- some good leads get missed because the scoring prompt is too strict
That is normal.
The fix is not to pretend the first version is perfect.
The fix is to review false positives and false negatives, then tighten the rules.
Where Hermes helps the most
After building this workflow, the highest-value wins were not flashy.
They were very practical:
1. Faster research per lead
Instead of opening ten tabs per company, Hermes can gather the first useful layer of context and summarize it.
2. More consistent filtering
Humans get tired and inconsistent. Agents do not get bored by the 37th company in the list.
If the criteria are clear, the system applies them the same way every time.
3. Better handoff into outreach
The output is not just “here are leads.”
It is “here are leads, here is why they passed, and here is what signal made them interesting.”
That gives you a much better starting point for personalized outreach.
4. Easier refinement over time
Because the workflow is explicit, you can improve individual steps:
- change the ICP
- tighten the search query
- adjust the scoring rules
- improve the prompt for enrichment
- add a human checkpoint only where confidence is weak
That is way easier than trying to fix a vague prompt that does everything badly.
Where the human still matters
This part is important.
You should not let the agent make the final call on every lead.
A good first-pass lead-gen agent should help with:
- research
- enrichment
- filtering
- scoring
- structured output
But a human should still own:
- final outreach priority
- strategic account selection
- nuanced judgment on edge cases
- messaging that depends on context the public web does not show
Lowkey, this is the difference between useful automation and lazy automation.
If you automate the research layer well, humans can spend more time on the part that actually moves pipeline.
What I would improve next
If I keep pushing this system, I would improve three things next:
1. Better source mix
Different lead sources behave differently. Some are broad but noisy. Some are high signal but smaller. I would keep tuning source mix by segment.
2. Stronger scoring memory
The more you review accepted and rejected leads, the better the agent can learn what “good fit” actually means for a specific niche.
3. Cleaner downstream integration
The ideal end state is simple:
- Hermes researches and qualifies
- only strong leads move forward
- output lands in a structured CRM or sheet
- outreach starts with better context already attached
That is the real win.
Not more leads.
Better starting points.
Final take
If you want to use AI for lead generation, I would not start with mass scraping or fully automated outreach.
I would start with a smaller question:
Can an agent help me find, filter, and explain better prospects than my current manual workflow?
That is a much more useful problem.
For me, Hermes fits well here because it is not just a text box. It can search, fetch, reason across sources, keep memory, and run repeatable workflows.
That makes it a solid base for lead generation workflows where context matters more than raw volume.
And honestly, that is the whole point.
The goal is not to look automated.
The goal is to spend less time digging through bad leads and more time talking to the right ones.
References
- Hermes Agent Documentation, Hermes Agent Documentation https://hermes-agent.nousresearch.com/docs/
- Hermes Agent Documentation, Features Overview https://hermes-agent.nousresearch.com/docs/user-guide/features/overview
- Hermes Agent Documentation, Scheduled Tasks (Cron) https://hermes-agent.nousresearch.com/docs/user-guide/features/cron
- Salesforce, BANT vs. MEDDIC: Comparing Sales Methodologies https://www.salesforce.com/blog/sales/bant-vs-meddic/
- 6sense, The Perfect Guide to Lead Qualification https://6sense.com/blog/the-perfect-guide-to-lead-qualification/