By Mehdi HassanAgent startups have a harder homepage job than most AI products. They're asking buyers to hand over work, not just try a feature, and buyers arrive skeptical. In a Crazy Egg study, 97% of respondents saw benefits in AI tools but only 45% said they would try one. This teardown looks at how six well-known agent companies close that gap on their homepages, as we read them in October 2026. It's a companion to our wider SaaS landing page examples and focuses only on autonomy, control and proof.
How do AI agent startups explain what their agent does on the homepage?
AI agent startups that explain their agent well name the job first, then show the agent doing it, then show the limits. Weaker pages lead with the word "autonomous" and leave the buyer to imagine what the agent touches and who checks its work.
Across the homepages below, three questions get answered in some order: what work does it take off my plate, how do I stay in control, and who else trusts it. The order and the weight vary with the buyer. Self-serve agents spend more space on the task run and a free start; enterprise agents spend more on controls, compliance and named customers. Our AI agent landing page template turns these questions into a section order.
Homepage | How it shows the work | How it shows control | Main CTA |
|---|---|---|---|
Lindy | Mock Slack replies listing tools, steps and result | Approval before irreversible actions | Free trial |
Devin (Cognition) | Workflow steps from plan to review | Review step, enterprise access controls | Sign up, demo second |
Fin (Intercom) | Long list of reasons with channels and actions | Testing suite, human handoff | Free trial, demo second |
Sierra | Platform modules and channels | Guardrails, change review, observability | Learn more |
Decagon | Sample concierge chats in the hero | Simulations, QA, permissions | Get a demo |
Relevance AI | Agent task dashboard and use cases | Levels of autonomy, approvals, audit logs | Book a demo |
How does Lindy show an agent doing real work?
Lindy shows its agent doing real work with a product mockup of Slack threads where the agent answers requests, lists the tools it used and the steps it took, and ends with a deliverable such as a report. The buyer sees inputs, actions and output in one glance.
Its hero frames the product as an AI employee you hire, then backs the framing with role pages such as paid ads, reporting and recruiting. The control story is placed where doubt peaks: example cards offer choices like pausing a campaign rather than acting on their own, and the security section states that irreversible actions wait for approval. Because the CTA is a free start, the page carries setup reassurance near the button. The lesson for other agents: show the receipt, not the robot.
How does Devin explain an autonomous software engineer?
Cognition's Devin explains autonomy through a workflow: understand and plan, code and test, review, then automate and maintain. The hero names the role directly, and each step says what the agent does, such as testing in its own browser and continuing until a pull request is ready.
The page relies on text blocks more than product visuals, which works for a technical buyer who wants to know where the agent runs and how it fits existing tools like Slack and CI. Control appears as a review step and an enterprise section covering audit logs, access controls and isolated sessions. A dated list of recently shipped updates acts as proof the product moves fast. The trade-off is that less technical buyers get fewer visual cues; our devtools landing page examples cover that audience in more depth.
How do Fin, Sierra and Decagon sell customer service agents?
Customer service agent homepages sell on resolution, safety and scale, because the agent talks to the buyer's own customers. Intercom's Fin, Sierra and Decagon all show channels, testing and enterprise proof, but each puts the weight in a different place.
- Fin uses a long numbered list of reasons to choose it, covering channels, the actions it can take in connected systems, a testing suite with simulations, and handoff to human teams with context kept. It talks about outcome-based pricing and links to a pricing page.
- Sierra presents a platform of named modules for building, optimizing and monitoring agents, with guardrails, review of changes before shipping and observability of every agent action. Proof is a dense customer logo wall, executive quotes and a long list of compliance standards.
- Decagon puts sample customer conversations straight into the hero, so the buyer sees the agent extending a rental or rebooking an appointment. It describes workflows written as natural-language procedures, plus simulations and always-on QA, and pairs named customers with outcome claims in testimonials.
The shared pattern is that control is shown as an operating system for the agent, testing before launch and monitoring after, rather than a single safety badge. If your agent faces customers, that's the bar buyers will compare you against.
How does Relevance AI explain levels of autonomy?
Relevance AI explains autonomy as a scale, with four levels running from assisted work to fully self-driving agents, and one level explicitly including a human review step. That framing lets buyers pick how much to delegate instead of accepting all-or-nothing autonomy.
The page also shows a task dashboard listing each agent with its connected apps, model and evaluation score, which makes the work feel measurable. A rollout timeline from mapping use cases to scaling a pilot answers the question of how adoption works. Enterprise controls, including human-in-the-loop approvals, role-based access and agent tracing, sit in their own section. It's a strong pattern for any agent sold into teams that need a gradual path.
Why autonomy levels work as a homepage device
Buyers rarely object to automation in general. They object to losing control of a specific step, such as sending an email to a customer or changing a budget. A levels framework turns that fear into a choice: start with the agent drafting and a person approving, then move up when trust is earned.
It also gives sales a script. A champion can tell their manager that the team will start at the assisted level, which is easier to approve than an agent that acts alone from day one. If your product supports approval settings, a simple levels graphic is often clearer than a paragraph about safety.
What do weak AI agent homepages get wrong?
Weak AI agent homepages describe intelligence instead of work. They promise a fully autonomous AI workforce, show an abstract animation of nodes, and never say which tools the agent touches, what it can't do or who reviews its output.
- Autonomy as the headline. "Autonomous agents for everything" says nothing about the job. Name the task.
- No task run. Without inputs, steps and an output, buyers can't picture the agent in their workflow. Our guide to AI hero sections shows formats that do this.
- Control buried in the footer. Approvals, permissions and data policy belong next to the claim they qualify.
- Proof that doesn't match the buyer. Big logos help enterprise buyers; a self-serve buyer needs a free start or a live playground.
What should an AI agent website copy from these homepages?
An AI agent website should copy the structure, not the styling: name the job, show a task run, put control next to the claim and match proof to the buyer. Work through it in this order.
Write the job in the headline
Say what work the agent completes, for whom, in plain words. Leave "autonomous" for the subhead if you use it at all.
Show one real task run
Inputs, the tools touched, the steps taken and the output, as a mockup, replay or short video.
Show where people stay in control
Approvals, handoff, testing and logs, placed right after the task run.
Match proof to the buyer
Named customers and compliance for enterprise; a free start and setup reassurance for self-serve.
Make the CTA fit the motion
Free trial when the agent can show value alone, a demo when setup needs your team.
