Agentic landing-page workflow

Build pages AI wants to recommend.

Give your coding agent feedback from ChatGPT, Claude, Gemini, and Grok. Find confusing claims and missing proof, make the changes, and retest to see whether the recommendation improves.

Connect once through MCP
Keep credentials in the browser

The complete loop

Test→Read→Improve→Deploy→Retest

Connect Agentzia to your coding agent

Add the Agentzia MCP server to Codex, Claude Code, Cursor, Grok Bot, or another OAuth-capable MCP client. Sign in through the browser when the client asks for approval.

https://www.agentzia.dev/mcp

Choose the page and AI clients

Check for an existing report before starting new research. For a new test, choose a public URL and the AI clients your customers use. Check credits and free capacity; one credit tests one channel across up to four buying situations.

list_runs → get_credits

Run the first test

Use a UUID requestId; reuse the same ID and arguments if the response is lost. Agentzia runs known-product, category-discovery, problem-led, and competitive-choice requests when the page provides enough public context.

analyze_url

Read the result in layers

Poll status at pollAfterMs until terminal, then check reportQuality and missing participants. Read summary and actions, follow nextOffset, and use evidenceCalls for exact responses and available traces. Reference checks show what Agentzia inspected; unresolved findings are in insights.

get_run

Separate page work from prerequisites

Implement the specific changes supported by the actions and their source evidence. Unresolved findings do not justify an edit. Treat customer proof, legal policy, earned distribution, and positioning decisions as prerequisites that need evidence or owner input.

get_run · section: actions

Ship one coherent release

Keep the test URL public and stable. Make related changes together, preserve the previous version in source control, and deploy so the next run evaluates the real page your customers and agents can reach.

External step · deploy

Retest the same clients

After an approved edit and deployment, create a new test with a new requestId and the same URL and clients. Use retry_run only to recover a failed or incomplete study. Read the comparison caveats before interpreting changes. Record which discovery, trust, and selection decisions moved and which remained unchanged.

analyze_url → get_run

Connect your client

Use the coding agent already working on your site.

Choose the matching setup, paste one command or configuration, and approve Agentzia in the browser. The client receives scoped access without seeing your login code or payment details.

01
Add the server
Copy the commands above into your terminal. They register Agentzia for this client and begin secure sign-in.
02
Approve sign-in
Your browser opens Agentzia. Sign in and approve the connection; your MCP client keeps the resulting OAuth session.
03
Start a fresh session
Restart any session that was already open so the client loads Agentzia's seven tools from the beginning.
04
Run your first test
Ask your agent: “Use Agentzia to analyze this landing page, explain the report, and propose the highest-priority improvements.”

Agentzia MCPConnect your coding agent

OAuth ready

Secure MCP endpointhttps://www.agentzia.dev/mcpStreamable HTTP

CodexTerminal

codex mcp add agentzia --url https://www.agentzia.dev/mcp
codex mcp login agentzia

Add Agentzia, then sign in securely in your browser. No token to copy.

Connect with Codex No API key or private token required.

A useful first request

“Use Agentzia to test this landing page with ChatGPT, Claude, Gemini, and Grok. Read the report, explain which findings can be implemented in the page, and show me the proposed changes before editing.”

The request names the outcome and leaves the research behavior to Agentzia. Your coding agent can then inspect the evidence instead of working from a copied summary.

Good work for the agent

Page structure and evidence presentation

Copy, hierarchy, navigation, page linking, metadata, schema, feature explanations, use cases, comparison structure, and public documentation.

Ask a human first

Claims that require authority

Customer outcomes, certifications, legal promises, data-retention policy, pricing decisions, founder identity, and new positioning commitments.

Before the first run

Common workflow questions

Should I test every AI client on every release?

Use the clients that matter for the release. A full four-client panel gives the broadest comparison. A single-client rerun is useful when a change targets one specific distribution or customer channel.

Can Agentzia test a local development URL?

The research clients need a public URL they can reach. Deploy a preview or staging page that contains only public marketing information, then test that URL.

What should the coding agent change automatically?

It can safely implement page content, hierarchy, navigation, metadata, structured data, and internal-link changes that the report supports. It should ask for input before inventing customer claims, legal policies, prices, certifications, or positioning decisions.

How do I know whether a change worked?

Run the same clients and buying situations after deployment. Compare the preserved decisions, research evidence, and page reach. A changed outcome is meaningful when the underlying answer and evidence changed too.

MCP referenceTools, setup, authentication, and report sections Complete examplesRead five reports with all responses and evidence Product landscapeCompare decision testing with AI visibility monitoring

Start with the public page you have today

Stop guessing whether the new page is better. Test it.

Connect Agentzia