Watch AI agents such as Muse, Grok Bot, Instinct and Dot find, pick and use your API or MCP
Agent Lab gives real coding agents an unbranded task in your category, then records what they search for, what they install and whether the integration works. You get the fixes, tested before you ship them.
Preview of the AINET Agent Lab dashboard with sample data: how often agent search finds the product, how often coding agents pick it, verifier pass rate, selection rate over time against competitors, results by agent, and a ranked list of fixes with paired A/B results.
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Your next users ask an agent first
Developers now hand the integration to a coding agent: find a price feed, wire it in, ship it. The agent picks the API, and the developer may never see the alternatives. AINET shows you whether it picked yours.
Three tests every coding agent puts your API through
Before a developer reads your homepage, their agent has already searched for a tool, settled on one and tried the first call. None of it shows up in your analytics.
Discovery: do you rank in agent search?
We send unbranded category queries through the search APIs agents call, such as Exa, Tavily and Brave, and record whether your docs or MCP listing come back, and how high.
Selection: are you the tool they install?
Claude Code and Codex get a starter repo and a task that never names you. The transcript shows what they read, what they installed, and whether they wrote their own instead.
Usage: does the first run succeed?
With your MCP server or SDK behind our recording proxy, we check tool choice, arguments, errors and retries, and run payment steps on fixtures or a testnet.
Find the step that loses the task. Then fix it.
Every run is stored as a transcript, so each number traces back to what an agent actually did.
Win and loss, task by task
How often agents pick you for each task, who they pick instead, and how often they write the code themselves.
Competitor changes, matched to agent picks
When a rival ships an SDK, an llms.txt or a registry listing, see whether agents' choices moved with it.
One funnel per task
Found in search, picked, called with valid arguments, passed the verifier. The biggest gap is where to start.
Test the fix before you ship it
We rewrite the tool description or doc on our proxy and run paired A/B tasks, so you see the lift before you deploy.
From a task list to a tested fix
Write down what an agent should be able to do with your product, run real agents against it, and prove each fix with a paired retest.
Write tasks. List the jobs a developer would hand an agent in your category, with a persona, the rivals in play and a script that checks the result.
Built on real agent runs
Real harnesses, real search APIs, raw transcripts kept. Every rate comes with the runs behind it and a confidence interval.
Know where agent search puts you
Your rank for unbranded category queries in Exa, Tavily, Brave, Perplexity Search and the other search APIs agents call.
See which agents pick you
Selection rate for each coding agent on your tasks, against the category average, and who they pick when it isn't you.
Agent traffic
See which AI crawlers and agents read your docs and hit your endpoints, verified by user agent and IP.
Rival benchmarks
The same tasks, rotated so no product is always listed first, against up to five rivals you choose.
Fact check
Catch agents stating the wrong price, rate limit or endpoint, checked against facts you give us.
Payment steps
Check that an agent can get through an x402 payment, run on fixtures or a testnet, never real funds.
Bring your own GEO stack. We handle the agents.
GEO dashboards are everywhere now. Keep the one you use, bring your own model API keys, and add AINET's agent runs on top.
Keep your GEO tool
Nothing to migrate. Send us its prompt list and competitor set, and we turn them into agent tasks.
Bring your own API keys
Agent runs bill to your own Anthropic, OpenAI or Google API keys at the provider's rates, with a spend cap on every run.
Cover what GEO can't
GEO tools report what chatbots say about you. AINET records what coding agents do: what they search, what they install and whether the integration works.
Pricing
Request a trial- Public agent leaderboard by category
- Top-level selection scores
- 7-day history
- Trending gainers and losers
- Agent runs on Claude Code, Codex, Cursor and Gemini CLI
- Discovery, selection and usage tracks
- Competitor benchmarking, up to 5 rivals
- Ranked fixes with paired A/B retests
- Bring your own model API keys
- 90-day history, weekly report and API access
- Unlimited tasks, agents and rivals
- Custom agent personas and harnesses
- White-label reports
- SSO, SLA and priority support
Not sure yet? We'll run a free agent report on one of your API tasks.
Get a free reportGet your free agent report
We run one of your tasks through real coding agents and show you who they picked, where the runs failed and which fixes helped. Then decide if you want the full platform.
From the blog
Guides and perspectives on how agents choose and use APIs.
FAQs
AINET's Agent Lab runs real coding agents, such as Claude Code, Codex, Cursor and Gemini CLI, on unbranded tasks in your category. It records whether agent search finds your API or MCP server, whether the agent picks it, and whether the integration works, then ranks fixes and tests them in paired runs.
Get chosen by agents
See what coding agents do with your API before they choose for your users.