Walid Boulanouar recently shared something that caught my attention: "ok, so I built a claude code (browser ) agent that finds domains under $10 and buy them for me using claude code extension." He added that "everything starts with "ay" +" and then listed the core behaviors: running autonomously, trying dozens of combinations, researching web forums, using open source AI models only (Minimax), and costing $1.8/day to run.

That short post is a great snapshot of where AI automation is heading in 2026: less "prompting" and more end-to-end systems that search, decide, and act. In this article, I want to expand on what Walid built, why it works, and what you should think about before you build your own domain-finding and buying agent.

The idea: an agent that scouts and buys low-cost domains

Walid's premise is simple: let an autonomous browser agent scan for available domains under a price threshold (in his case, $10), then purchase them automatically.

What makes it interesting is not the basic domain search. People have done domain research forever. The twist is automation plus iteration: an agent can generate many name combinations, validate them quickly, check signals of demand, and execute a purchase without waiting for a human to click through a registrar checkout flow.

If you've never built agentic workflows before, it helps to think of this as a pipeline:

Generate candidate names
Check availability and pricing
Evaluate demand signals
Decide whether to buy
Purchase and record the asset

Walid's post hints at each step.

What "running autonomously" actually implies

Walid said the agent is "running autonomously ( using Ralph )". Autonomy is not a single feature, it is a set of design choices:

A scheduler or loop so the agent runs continuously or on a cadence
A state store so it remembers what it has tried (to avoid repeats)
Guardrails so it does not purchase garbage or overspend
Observability so you can audit decisions and stop it when it drifts

In practice, true autonomy requires you to decide what the agent is allowed to do without permission. Domain purchasing is high risk because it directly spends money. Most teams add a manual approval step at first (human-in-the-loop), then gradually relax it as they see stable results.

Key insight: autonomy is earned. You start supervised, then promote the agent as your confidence grows.

Name generation: "dozens of combinations" with constraints

Walid mentioned "trying dozens of combinations ( based on demand 1,2 words max )". That constraint matters.

Short names have advantages:

Easier to remember and type
More brandable
Often better resale potential

But short also means scarce, so the agent must be creative. A realistic generator might combine:

High-intent keywords (like "pay", "vault", "ship", "clinic")
Industry nouns ("fleet", "studio", "ledger")
Modern suffixes/prefixes ("hq", "ai", "labs")
Spelling variants (carefully, to avoid spammy vibes)

The best agents do not just generate random combinations. They use demand signals to bias the search. For example, if forums and communities are buzzing about "voice agents", the generator leans into those terms.

Researching forums: finding demand before buying

Walid wrote that the agent is "researching the web forums". This is arguably the most valuable step. Availability alone is not an edge. Demand is.

Here are forum-based signals an agent can extract:

Repeated phrases and problem statements ("need a tool for...")
Newly emerging acronyms or shorthand terms
Requests for alternatives to a popular product
Funding announcements or launches that create naming trends

The challenge is avoiding noise. Forums are full of hype cycles. A strong approach is to measure persistence: does a topic appear across multiple communities and keep showing up over several days or weeks?

A simple scoring model might include:

Frequency: how often a keyword appears
Breadth: how many unique sources mention it
Recency: how recently it appeared
Intent: whether posts show buying intent vs casual chatter

Why open source models matter here (and what it changes)

Walid also said he's "using open source ai models only ( Minimax )". Using an open or self-hostable model can reduce cost, increase control, and limit data exposure.

For a domain-buying agent, model choice affects:

Cost per run: can you afford continuous exploration?
Latency: can the agent iterate quickly?
Tool use: can the model reliably follow browser actions and structured prompts?
Safety: can you constrain output and decisions?

If the agent is mostly doing extraction (forum scanning) and light reasoning (scoring and filtering), an open model can be more than enough.

The economics: $1.8/day is the real story

Walid said it costs "$1.8/day to run". That number invites a basic ROI calculation.

At $1.8/day, you are spending about $54/month on operation. The bigger cost is domain purchases themselves. If the agent buys, say, 30 domains/month at $10 each, that is $300 plus $54 operating cost.

Now the business question becomes: can you sell enough of those domains at a markup to cover both carrying cost and inventory risk?

Walid hinted at the next step: "will setup another one that reaches out to potential buyers offering it for 500$/domains".

A few important points if you want to replicate that idea:

Not every domain is worth $500. Most are not.
Outbound selling is a different skillset than buying.
Your holding cost increases over time (renewals, opportunity cost).

The agent can help by keeping a clean ledger: purchase date, rationale, estimated buyer personas, and a shortlist of target companies.

Building the outreach agent: what to automate (and what not to)

An outreach agent can do a lot, but it should not spam.

Good automation targets:

Identify likely buyers (startups in a niche, companies launching products, agencies)
Find decision-maker contact channels (public emails, contact forms, LinkedIn profiles)
Draft personalized messages based on context (what the company does and why the domain fits)
Track replies and stop outreach when asked

What should stay cautious:

Bulk emailing without compliance checks
Misrepresenting ownership or urgency
Over-automating personalization so messages feel fake

If you do outreach, put guardrails in place: rate limits, opt-out handling, and a requirement that each message references a real reason the domain is relevant.

Guardrails and ethics: just because an agent can buy does not mean it should

Domain flipping sits in a gray zone for some people. It is legal, but it can feel extractive if the approach is purely speculative and blocks legitimate builders.

So if you are building a system like this, I think it is worth setting rules that align with your values:

Avoid trademarks and brand confusion
Avoid buying names that clearly target existing brands
Prefer names tied to generic concepts and emerging categories
Be transparent in outreach: you own the domain and are offering it

Automation amplifies behavior. If your process is sloppy, your agent will scale that sloppiness.

A practical blueprint (if you want to build your own)

If I were turning Walid's post into a build plan, it would look like this:

Define constraints: price ceiling, word count, TLDs, exclude list
Build a generator: keyword sources plus combination rules
Add demand scoring: forum scraping plus a scoring rubric
Add availability checks: registrar APIs or browser checks
Add a buy decision policy: thresholds, daily budget caps
Add approvals: start with manual confirmation
Add logging: every buy must have a traceable rationale
Iterate weekly: analyze which buys were mistakes and update filters

This is also why Walid's last line matters: "will share how it goes in the coming days." The real skill is not building the first version. It is improving the decision policy after you see results.

What Walid's post reveals about modern content strategy

Even beyond the domain agent itself, this is a strong example of creator-led engineering content:

It is specific (tool, task, cost)
It is measurable ($1.8/day)
It teases an experiment (results later)
It signals a roadmap (next agent for outreach)

That combination is why posts like this travel: they are both practical and open-ended.

This blog post expands on a viral LinkedIn post by Walid Boulanouar, building more agents than you can count | aiCTO ay automate & humanoidz | building with n8n, a2a, cursor & ☕ | advisor | first ai agents talent recruiter. View the original LinkedIn post →