Best AI Agent Websites: 10 Examples That Get It Right

Best AI Agent Websites: 10 Examples That Get It Right

AI agent websites have a difficult job.

Most websites are explaining something people already understand.

A plumber fixes plumbing problems. A law firm provides legal services. An ecommerce website sells products.

AI agents are different.

Even if somebody understands the general idea, they may still have questions almost immediately.

What does the agent actually do?

How autonomous is it?

What tools can it access?

Does it work by itself or wait for instructions?

What happens if it makes a mistake?

Where does the human come back into the process?

That makes AI agent website design particularly interesting to me.

The strongest sites are not necessarily the ones with the most futuristic animation or the biggest glowing gradient. They are the ones that make an unfamiliar product feel understandable.

The ten websites below all approach that problem differently.

I am looking primarily at the website design, positioning and way the product is explained, rather than trying to rank the underlying AI products themselves.

What makes a good AI agent website?

Before getting into the examples, there are a few things I think particularly matter in this category.

A strong AI agent website should make it reasonably easy to understand:

• What the agent actually does

• Who it is designed for

• What happens when somebody gives it a task

• Which tools it can interact with

• How much control the user keeps

• What happens when human approval is needed

• Why somebody should trust it with real work

Product demonstration is particularly important.

There are only so many times an AI website can say things like autonomous, intelligent and agentic before those words stop meaning very much.

Showing a workflow is usually much more convincing.

1. Lindy

Lindy is probably one of my favourite examples of simplifying a complicated category.

Rather than leading with technical terminology, the website describes Lindy as an AI teammate that plugs into the tools you already use, learns how you work and takes work off your plate.

That is much easier to understand than starting with an explanation of agent architecture.

The rest of the page then gradually adds detail.

It shows Lindy working across tools such as Slack, Gmail, Notion and HubSpot, handling scheduled tasks, accessing shared knowledge and carrying out work on behalf of the team.

What I particularly like is that the site uses familiar workplace situations to explain the technology.

Instead of:

“Our autonomous agent orchestration layer enables cross-platform workflows.”

you essentially get:

“Ask Lindy to do the thing you would normally ask a teammate to do.”

Much better.

What Lindy gets right

The concept is explained before the technology.

Visitors can understand the product at a human level first and discover the technical depth afterwards.

That is something a lot of AI startups could learn from.

Visit Lindy → Lindy

2. Gumloop

Gumloop takes a slightly more infrastructure-focused approach.

Its website positions the product around building, sharing, optimising and controlling agents for work.

The clever part is how quickly the site gets into what those agents can actually do.

You see examples such as a CRM agent, recurring tasks, connectors and agents operating a real browser.

The browser section is particularly good because it addresses one of the biggest questions around autonomous software:

What does it actually mean for an agent to use another website?

Gumloop explains that its agents can open a browser, click, type, upload files and interact with sites even when there is not a conventional API available. It also explains handoffs, saved credentials and session recording.

That is considerably more useful than showing another abstract animation of glowing nodes connecting together.

What Gumloop gets right

It explains capability through behaviour.

Instead of repeatedly telling you that the agents are powerful, the website shows the kinds of things they can physically do.

That is exactly how complicated AI products should be explained.

Visit Gumloop → Gumloop

3. 11x

11x does something I would love to see more AI-agent websites attempt.

It lets you actually experience the product.

The company offers digital workers focused particularly around sales and revenue operations, including Alice, an AI SDR, and Julian, an AI phone agent.

The website includes an interactive experience where visitors can talk to Julian directly in the browser.

You choose a scenario and voice and can have an actual conversation with the agent.

That is extremely effective product marketing.

A page could spend 1,000 words explaining how natural an AI phone agent sounds.

Or it can let you call it.

11x chooses the second option.

The wider page then backs that experience up with customer results, product screenshots and a clearer explanation of how its digital workers fit into existing revenue workflows.

What 11x gets right

The demo is part of the website.

For products where the experience itself is the selling point, this is often stronger than screenshots or video.

If someone can safely experience part of the agent before signing up, let them.

Visit 11x → 11x

4. Relevance AI

Relevance AI feels more like an infrastructure platform than a single AI assistant, and its website reflects that.

The product is positioned around building, running and managing agents at scale.

What I like is that the site acknowledges the harder questions quite early.

How do you trigger agents correctly?

How do multiple agents work together?

How do you evaluate them?

How do you securely connect agents to company systems?

Those are much more interesting questions than simply saying businesses can “automate repetitive work.”

The site then presents the different parts of its platform as one connected system, covering triggers, context, orchestration, model routing, evaluations and monitoring.

It also has useful proof behind the claims, including customer examples showing measurable changes in pipeline, conversions and time saved.

What Relevance AI gets right

It doesn't hide the complexity. It organises it.

If your audience is technically sophisticated, simplifying everything until it becomes meaningless can be just as damaging as making the product too complicated.

Relevance AI gives different levels of information without expecting the homepage headline to explain the entire platform.

Visit Relevance AI → Relevance AI

5. Sierra

Sierra is an interesting contrast.

Its website has to sell AI agents to very large organisations, where excitement around AI is probably less important than reliability, integration and control.

The product focuses on customer-facing AI agents that can operate across voice, chat, email and WhatsApp, while connecting into existing systems to complete tasks rather than simply answer questions.

The website therefore puts a lot of emphasis on practical enterprise concerns.

It explains channels, languages, integrations, complex use cases and how agents interact with existing systems.

It also leans heavily on trust signals.

That changes the tone considerably.

The site does not need to convince you that AI is exciting.

It needs to convince you that deploying it across a large company is realistic.

What Sierra gets right

The design and messaging fit the buyer.

An enterprise AI site should probably not communicate in exactly the same way as an experimental startup selling to developers.

Sierra understands that.

Visit Sierra → Sierra

6. Harvey

Harvey is probably one of the best examples of a vertical AI agent website.

Rather than trying to build a general-purpose AI product for everyone, Harvey is designed around legal and professional work.

That gives the website a much more specific vocabulary.

The homepage talks about legal organisations, matters, research, contracts, documents and workflows rather than abstract AI capabilities.

Its dedicated Agents section is even stronger.

Harvey explains that agents can handle legal work end-to-end, run multiple tasks in parallel, work on schedules and return review-ready outputs.

Crucially, it also talks about human review.

Users can inspect the plan, adjust the scope and approve work, while agent activity is logged and outputs can be audited.

That kind of information is especially important for a product operating in a high-trust environment like law.

What Harvey gets right

It explains AI through the industry it serves.

The website rarely needs to say:

“Look at our amazing AI.”

Instead it can say:

“Here is how this changes legal work.”

That is much stronger positioning.

Visit Harvey → Harvey

7. CrewAI

CrewAI takes the developer-first route.

Its open-source platform focuses on creating and orchestrating multi-agent systems and is very clearly designed for people who are comfortable with technical language.

That means the website does not over-simplify the product.

It talks openly about planning agents, tools, MCP support, sandboxing, code execution and multi-agent orchestration.

For the intended audience, that is a strength.

There is a tendency for technology websites to assume that simpler language is always better.

It isn't.

The goal should be appropriate clarity.

A developer researching an agent framework probably wants to know what tools are supported and how the architecture works.

They do not necessarily need a cartoon character explaining what AI is.

What CrewAI gets right

It respects the technical audience.

Good website design is not about removing complexity.

It is about presenting the right complexity to the right person.

Visit CrewAI → CrewAI

8. Manus

Manus goes in almost the opposite direction.

Its homepage begins with a very familiar AI interaction:

“What can I do for you?”

The product then presents example actions such as creating slides, building websites, creating games and designing things.

That makes the site feel much closer to the actual product.

Rather than explaining the agent through diagrams first, the website puts the visitor into the same mental model as using it:

Tell it what you want done.

The product has grown into a broad platform with design, slides, browser operation, research, video, development and other capabilities, but the homepage keeps the starting point extremely simple.

What Manus gets right

The website behaves conceptually like the product.

That is an interesting design principle.

If your product is conversational, task-led or interactive, the marketing website can borrow some of that behaviour.

The visitor understands how the product works before they have even created an account.

Visit Manus → Manus

9. Sintra

Sintra solves another common AI-agent problem:

How do you make several different agents understandable without making the product feel like a technical control panel?

Its answer is to present them more like a team.

Sintra describes its product as pre-built AI agents that can handle areas such as reporting, support and administrative tasks while connecting to tools including Gmail, Outlook and Notion.

The idea of different AI helpers gives users a fairly immediate mental model for what the product does.

The website also makes the distinction between a chatbot and an agent explicit: the agent receives a goal, plans actions, uses tools and carries out work with less human input.

That kind of educational content matters in a category where customers may still be working out what an “agent” actually means.

What Sintra gets right

It creates a simple concept around multiple AI capabilities.

Giving different functions identities or roles is not appropriate for every AI product, but when done well it can make a complex platform easier to navigate.

Visit Sintra → Sintra

10. Artisan

Artisan's website is a strong example of designing around one very clear outcome.

Its AI sales agent, Ava, is presented as an autonomous BDR that finds prospects, writes personalised outreach, handles replies and books meetings.

The site then explains that process in sequence.

First Ava finds and qualifies leads.

Then she researches them and creates outreach.

Then she handles replies and books meetings.

That flow makes the autonomous part much easier to understand.

The site also handles another important AI-agent concern well: control.

Users can decide how much autonomy Ava has, from approval-heavy workflows to more automated campaigns.

That is useful because many customers are not really asking:

“Can the AI do this?”

They are asking:

“What happens if I let it?”

What Artisan gets right

It explains both the automation and the guardrails.

For agent products, showing what users remain in control of can be just as important as showing what the AI can automate.

Visit Artisan → Artisan

What the best AI agent websites have in common

Looking across these examples, several patterns appear again and again.

They show what the agent does

The strongest sites use:

• Product UI

• Interactive demos

• Example tasks

• Workflows

• Integrations

• Before-and-after processes

• Real outputs

That gives the visitor something concrete to understand.

An animated glowing sphere might look nice, but it usually cannot explain a product.

They sell the outcome before the architecture

Lindy sells the idea of a teammate.

11x sells more sales activity.

Harvey sells better legal work.

Artisan sells booked meetings.

Only after establishing the outcome do the sites explain how the technology makes it happen.

That order matters.

They make autonomy understandable

“Autonomous” sounds impressive until somebody considers giving software access to their CRM, inbox or customer database.

Good agent websites therefore explain things such as:

• Permissions

• Human approvals

• Escalation

• Audit logs

• Security

• Scheduling

• Limits

• Data access

Trust is part of the product.

It should therefore be part of the website.

They use familiar concepts

Coworkers.

Teams.

Tasks.

Workflows.

Campaigns.

Meetings.

Cases.

These ideas give people something familiar to attach an unfamiliar technology to.

That is usually more effective than inventing another piece of AI terminology.

They don't all look the same

This is important.

There is already an established visual stereotype for AI websites:

Dark interface.

Purple or blue gradient.

Glowing object.

Grid background.

Tiny dots moving between nodes.

None of those things are inherently bad.

But they have become common enough that simply using them no longer makes a company look particularly innovative.

The more interesting AI websites build their visual identity around the actual product.

How should you design an AI agent website?

If I were designing one from scratch, I would normally start with five questions.

1. What does the agent actually do?

Not what technology powers it.

What does somebody give it, and what comes back?

2. Can we demonstrate that?

If the product can safely be experienced inside the website, that can be incredibly powerful.

If not, a realistic interactive simulation, workflow or product demo may do the job.

3. What needs explaining before someone trusts it?

This could include security, data access, approvals, integrations or the limits of the agent.

Those questions should influence the site architecture.

4. Who is evaluating it?

A developer, small-business owner and enterprise procurement team need very different amounts of information.

Design around the actual buyer.

5. What makes this AI company visually identifiable?

This is the bit I think is going to become increasingly important.

As more agent products launch, simply looking “like an AI startup” is going to become less useful.

A recognisable brand will matter more.

AI agent websites are becoming their own design category

I think this is one of the more interesting areas of web design at the moment.

The technology is changing quickly, but so are the conventions around how it is explained.

A few years ago, an AI product might have been presented largely like conventional SaaS with an extra chatbot screenshot.

Agent products now have to communicate something more complicated.

They can act.

They can use tools.

They can make decisions.

They can sometimes operate without somebody prompting every step.

That creates new design problems around workflows, control, trust and demonstration.

And new design problems usually create more interesting websites.

If you're building an AI agent, agentic SaaS product or automation platform and need a website that makes the product easier to understand, I also offer AI Agent Website Design.

The goal isn't to make another website that simply looks like AI.

It's to make the product make sense.