LAST UPDATED: FEBRUARY 7, 2026
The hard part wasn't building it. The hard part is getting anyone to use it. Here's how agent builders are getting their first users — from cold start to real traction — and why the distribution playbook for AI agents looks nothing like traditional SaaS.
Software distribution has a well-worn playbook: launch on Product Hunt, write SEO content, run ads, build integrations. AI agents break this playbook in a specific way — they require trust before adoption. When you ask someone to use a traditional SaaS tool, you're asking them to input data and get output. When you ask someone to use an AI agent, you're asking them to delegate decisions. That's a fundamentally different ask, and it changes everything about how you acquire users.
The trust barrier means your first users almost always come from personal relationships, not marketing funnels. Someone has to believe your agent will act in their interest before they'll give it access to their calendar, their code repository, their customer conversations, or their financial data. This is why most successful agent builders report the same pattern: the first 10 users come from people who trust the builder, not the agent.
The good news is that once you cross the trust threshold, agents have a distribution advantage that traditional software doesn't: they produce visible results. A coding agent that ships a PR, a customer service agent that resolves a ticket, a research agent that delivers a report — these create artifacts that other people see. Your agent's output becomes your marketing.
Forget scale. Forget growth hacks. Your first 10 users are people you personally convince to try your agent, and that's exactly how it should work. The goal at this stage isn't growth — it's learning whether your agent actually solves a problem worth solving.
The most successful agents start as tools their builders use daily. If you built a research agent, use it for every research task you encounter for two weeks before showing it to anyone else. Document what works, what breaks, and what surprises you. Your first demo should be showing someone a real result your agent produced for you, not a hypothetical scenario. "Look what this did for me yesterday" is infinitely more persuasive than "imagine what this could do for you."
Send a message — not a mass email, an actual personal message — to 20 people who have the exact problem your agent solves. Tell them what it does in one sentence, show them one real result, and ask if they'd try it for a week. Expect a 25-50% response rate from people who know you. If you can't find 20 people with the problem, that's useful information too. A good template: "I built an AI agent that [specific thing]. Here's what it produced for me [link/screenshot]. Would you try it for a week and tell me what breaks?"
Join three to five communities where your target users already gather — Discord servers, Slack groups, subreddits, or niche forums. Spend two weeks being helpful before mentioning your agent. Answer questions, share knowledge, build a reputation. When you do mention your agent, frame it as a solution to a specific problem someone posted about. "I actually built something for this" hits different when people already know you're knowledgeable and helpful.
Key insight: Your first 10 users should each get white-glove treatment. Set up their accounts personally. Watch their first session. Ask them what confused them. This is the cheapest, most valuable research you'll ever do — and these early users become your advocates when you're ready to scale.
Once you have 10 users who genuinely rely on your agent, you've validated something real. Now it's time to make your agent discoverable beyond your personal network. This is where most agent builders stall — they try to jump straight to paid acquisition or viral mechanics without building the foundations that make those channels work.
Agent directories and registries are the equivalent of app stores for AI agents. They're where people go when they're actively looking for an agent to solve a specific problem — which makes them the highest-intent traffic you can get. List your agent on every relevant directory: specialized platforms for your domain, general AI agent marketplaces, and open registries like agent listing platforms that let users browse and compare. A complete listing with clear descriptions, real examples, and honest capability statements outperforms a flashy listing with vague promises.
Content marketing for agents is different from content marketing for SaaS. Instead of writing about what your agent could do, publish what it actually did. If your agent writes code, open-source a project it contributed to. If it does research, publish a report it generated (with appropriate editing). If it handles customer service, share anonymized metrics showing resolution times and satisfaction scores. Real outputs build credibility in a way that feature lists never will.
Pick the platform where your target users already spend their time and build a native integration. A Slack bot, a VS Code extension, a Zapier connector, a Chrome extension — one excellent integration in the right ecosystem is worth more than a standalone product with a beautiful landing page. Distribution through existing platforms means your agent shows up where people are already working, not where they have to remember to go.
Your first 10 users know other people with the same problem. Ask them — directly, specifically — to introduce you to two people who might benefit. Don't build a referral program yet. Just send a personal message: "You've been using [agent] for two weeks now. Do you know anyone else dealing with [specific problem]? I'd love an intro." Personal referrals at this stage convert at 50-70% because they come with built-in trust from someone who's actually used your agent.
The jump from 100 to 1,000 is where your agent's reputation starts mattering more than your personal reputation. At this stage, most of your new users won't know you — they'll evaluate your agent based on what others say about it, what they can verify about its track record, and whether it integrates into their existing workflow.
People search for solutions to problems, not for agents by name. Build content around the specific problems your agent solves: "how to automate [task]," "best tools for [workflow]," "[pain point] solutions." Each piece should demonstrate your agent's capability through real examples while providing genuine value even to readers who never sign up. The content compounds over time — an article that ranks for a high-intent keyword delivers users every month without additional spend.
Ask your best users if you can document their results. A good case study follows a simple structure: what was the situation before the agent, what changed after, and what were the specific measurable results. "Reduced research time from 6 hours to 45 minutes" is more persuasive than any feature description. Publish these prominently and use them in every sales conversation.
As your agent interacts with more users, the question shifts from "does this work?" to "can I trust this?" Registering your agent with an identity registry creates a verifiable track record that new users can check before engaging. Think of it like reviews on a marketplace — except instead of star ratings, users can see your agent's actual interaction history, how long it's been operating, and whether its reputation was built organically or manufactured. Trust visualization tools make this transparency automatic rather than something you have to manually prove.
Find complementary agents or platforms and propose joint distribution. A coding agent and a code review agent serve the same users. A customer service agent and a helpdesk platform have natural synergy. Partnerships work when both sides gain distribution they couldn't get alone. Start with co-marketing (joint blog posts, shared case studies) before attempting deeper technical integrations.
Not all channels work equally well for AI agents. Here's what builders report about each channel's effectiveness at different stages, based on patterns across successful agent launches.
| Channel | Best Stage | Trust Level | Notes |
|---|---|---|---|
| Personal outreach | 0–10 | Highest | Your reputation carries the agent |
| Community participation | 10–100 | High | Earned credibility converts well |
| Agent directories | 50–500 | Medium-high | High-intent traffic, comparison shoppers |
| Personal referrals | 10–200 | High | Trust transfers from existing users |
| Platform integrations | 100–1K | Medium | Distribution through existing workflows |
| SEO content | 200–10K | Medium | Compounds over time, evergreen traffic |
| Case studies | 100–1K | Medium-high | Proof over promises |
| Identity registries | 100–10K | High | Verifiable track record for strangers |
| Paid acquisition | 500+ | Low | Works only after trust signals exist |
| Product Hunt / launches | 100–500 | Low-medium | Spike traffic, low retention without trust |
Broad launches feel exciting but usually produce a spike of curious visitors who bounce because the agent isn't tuned for their specific use case. A narrow launch to 50 people who desperately need what you built generates more learning, more retention, and more word-of-mouth than a Product Hunt launch to 5,000 strangers.
Waitlists made sense for infrastructure-constrained products. For most agents, a waitlist just adds friction between a curious user and the experience that might convert them. If you're using a waitlist to manage demand you don't have yet, you're optimizing for a problem that doesn't exist while ignoring the one that does — getting anyone to try your agent at all.
"Our agent uses GPT-4 with RAG and function calling" means nothing to the person trying to decide whether to let an AI agent handle their customer emails. "Resolved 340 support tickets last month with 94% satisfaction" means everything. Lead with outcomes, not architecture.
Your agent might be technically excellent, but if users can't verify its track record, they'll choose a worse agent with a visible reputation over yours. This is especially true for agents that handle sensitive tasks — financial data, customer communications, code deployment. Investing in verifiable identity isn't a nice-to-have; it's table stakes for agents that want to operate beyond their builder's personal network.
If your first 10 users aren't coming back regularly, adding 100 more won't fix the problem. Retention is the signal that your agent solves a real problem well enough that people integrate it into their workflow. Without retention, growth is just a more expensive way to learn you haven't found product-market fit yet.
Getting your first 10 users is a people problem — they trust you, so they try your agent. Getting to 1,000 is an infrastructure problem — strangers need a way to evaluate your agent without knowing you personally.
RNWY provides that infrastructure. When you register your agent, it receives a permanent, non-transferable identity token — a soulbound credential minted to your wallet that can't be sold or transferred to disguise a bad actor as a trusted operator. Your agent's reputation accrues transparently: interaction history, attestations from other users, and pattern data like wallet age and activity consistency that can't be manufactured overnight.
Instead of asking users to take your word for it, you give them data they can verify themselves. That's the difference between "trust me" and "check for yourself."
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Directories, registries, and marketplaces where your agent can get discovered.
Read guide →Pricing models, revenue streams, and what's actually working for agent builders.
Read guide →How fraud works in agent ecosystems and what verification infrastructure looks like.
Read guide →Register on RNWY and give your agent a verifiable identity that builds trust with every interaction.
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