Build an AI Agent for Partner Lead Research

Reader persona: a founder, growth lead, BD operator, or solo marketer who needs to find relevant partner targets without spending hours in spreadsheets.

Job to be done: use an AI agent to research potential partners, score fit, prepare outreach notes, and keep all external contact behind human approval.

Partnership work usually starts messy.

You have a rough idea:

Find newsletters, communities, agencies, creators, or tools that serve the same audience we serve.

Then the work becomes repetitive:

• search;
• open pages;
• read positioning;
• identify audience;
• check recent activity;
• collect contact paths;
• decide whether outreach is worth it;
• write a short note.

That is a good workflow for an AI agent.

The agent should not spam people.

It should prepare a clear partner lead list for a human to review.

The workflow

Use this shape:

partner hypothesis → search → qualify → score → prepare notes → human approval → outreach

The agent does the research and structuring.

The human decides who to contact and what to send.

Step 1: Define the partner hypothesis

Do not start with “find partners.”

Start with a sharper hypothesis.

Example:

Find small AI workflow consultants, automation agencies, and operator-focused newsletters whose audience wants AI agents to do practical business work.

Exclude:
- generic AI news accounts;
- crypto trading communities;
- large enterprise consultancies;
- inactive sites;
- low-quality directories.

This helps the agent search with judgment.

Step 2: Create a lead table

Ask the agent to create a table like this:

| Name | Type | URL | Audience | Why fit | Recent signal | Contact path | Fit score | Notes |
|---|---|---|---|---|---|---|---|---|

Define the fields:

Name: partner/company/person name.
Type: newsletter, community, agency, creator, SaaS, consultant, directory.
Audience: who they serve.
Why fit: why their audience overlaps with ours.
Recent signal: recent post, launch, newsletter issue, GitHub activity, event, or product update.
Contact path: public email, form, social profile, community, or intro path.
Fit score: 1–5.
Notes: anything a human should know before outreach.

The “recent signal” field matters.

It stops the list from becoming stale directory scraping.

Step 3: Give the agent scoring rules

Use simple scoring:

# Partner fit score

5 — Strong audience overlap, active, credible, clear collaboration path.
4 — Good fit, active, but outreach angle needs refinement.
3 — Possible fit, but audience or activity is mixed.
2 — Weak fit or unclear audience.
1 — Not relevant, inactive, spammy, or risky.

Ask the agent to explain every score.

For each lead, include one sentence explaining the score.
Do not assign a high score without evidence.

Step 4: Ask for a small batch first

Do not ask for 500 leads.

Ask for 20.

Find 20 potential partner leads.
Prioritize quality over volume.
Use public information only.
For each lead, include source URLs and a one-sentence score rationale.

A small batch is easier to review.

If the first batch is good, you can repeat the workflow weekly.

Step 5: Use BrowserMan when research needs a real browser

Some partner research requires logged-in or dynamic pages.

BrowserMan can let an agent inspect approved pages in your real browser session.

Use it for:

• checking a newsletter landing page;
• reading public posts;
• inspecting creator profiles;
• reviewing community pages;
• verifying that a contact form exists;
• collecting screenshots for review.

Keep the safety rule explicit:

You may read approved public pages and prepare notes.
You may not follow, like, comment, DM, submit forms, subscribe, or send outreach without approval.

That keeps research separate from external action.

Step 6: Prepare outreach notes, not sends

After the agent builds the list, ask:

For the top 5 partner leads, prepare outreach notes.

For each lead, include:
1. why this partner is relevant;
2. one specific recent signal to mention;
3. a possible collaboration angle;
4. a short draft message;
5. risks or reasons not to contact.

Do not send anything.

A useful draft should feel specific.

Bad:

Hi, I love your work. Want to partner?

Better:

Hi [Name] — I saw your recent piece on AI workflows for solo operators. It overlaps with what we’re building: managed OpenClaw/Hermes agents that help non-technical teams run repeatable work from chat.

If useful, I can put together a short first-run guide for your audience showing how to set up a practical agent workflow without managing a server.

The human should edit before sending.

Step 7: Save the research as a repeatable job

Ask the agent to save the workflow:

partner-research/
  README.md
  scoring-rules.md
  2026-05-04-partner-leads.md
  outreach-drafts.md

Next week, the agent can compare:

• which leads were contacted;
• who replied;
• which partner types had the best fit;
• what outreach angles worked;
• what should be excluded next time.

This turns partner research into an operating loop.

Step 8: Run it with ClawMama

If you already run OpenClaw or Hermes, build this workflow there.

If you want a ready-to-use agent, create one with ClawMama:

https://t.me/clawmamarun_bot

ClawMama gives you a managed OpenClaw or Hermes agent. New users get $2 of AI credits and access to the latest ChatGPT model, so you can test the partner research workflow before maintaining your own runtime.

A good first message is:

Help me build a partner lead research workflow.

Create:
1. a partner hypothesis template;
2. a lead scoring rubric;
3. a research table format;
4. a top-5 outreach notes format;
5. approval rules before any external action.

Use public information only unless I approve browser access.
Do not contact anyone, submit forms, DM, follow, like, or post without approval.

What not to automate

Do not let the agent blindly:

• send cold emails;
• submit contact forms;
• DM people;
• scrape private communities;
• collect personal data beyond public business contact paths;
• claim partnership interest without evidence;
• add people to mailing lists;
• impersonate you.

Partner work depends on trust.

The agent should make research faster, not make outreach careless.

Final takeaway

A partner research agent should produce a better shortlist:

who → why fit → evidence → contact path → draft → approval

OpenClaw and Hermes provide the runtime. ClawMama makes it easy to start. BrowserMan can add controlled browser research when the work requires real pages.

The output should be a reviewed partner pipeline, not automated spam.