Otto is a team of AI agents that plans, writes, runs, and heals your end-to-end tests, learns from every run, and grades its own work — turning a plain-English goal into coverage you can trust.
Each agent owns one job and hands off to the next. You give a goal; the team does the rest — from deciding what to test to grading its own work.
Reads your app to learn its stack, structure, and where sign-in lives.
Turns your PRDs and specs into prioritized candidate tests, with coverage gaps flagged.
Designs each test: the steps, the data it needs, and what counts as success.
Writes the test by exploring the live app — no recording, no scripting.
Repairs tests when the UI changes, then re-runs to confirm the fix.
Turns every run into lessons, and keeps the ones that make the next run better.
Scores how well every agent planned, wrote, and healed each test.
Point Otto at your PRDs, design docs, and specs. It reads them and proposes the test cases they imply — each prioritized and mapped against what's already covered, so decision-makers see the gaps that carry the most deployment risk instead of finding them in production.
Soon Pull requirements & production defects straight from Jira.Selectors move, workflows evolve, interfaces get redesigned — and normally someone spends their Friday fixing red automation. Otto diagnoses the break, repairs the test on its own, and re-runs to confirm the fix still checks the same outcome.
Most automation decays — every UI change chips away at it. Otto does the opposite: it turns each run into lessons, keeps only the ones that prove they make the next run better, and gets steadier the longer it tests your app. Coverage that compounds instead of rotting.
/api/cart to settle before asserting the totalWhen the testing decisions are made by AI, you need to know those decisions were any good. Otto's Auditor scores every plan, test, and repair on a 0–100 quality index, and flags anti-patterns like empty or vacuous checks — so you can trust the autonomy instead of taking it on faith.
We run every agent on top-tier models and keep you current as better ones ship — higher plans get the newest models first. On a dedicated or Enterprise deployment, bring your own models, keys, and private endpoint, so you control which AI your data is sent to — right down to a fully private, in-environment model.
Real apps live behind a login and run on real data. Otto signs in as each user role your tests need — with stored credentials, multi-step flows, and ready-made recipes for identity providers like Okta and Login.gov.
Bring your test data as a simple table or a CSV upload, and mark rows single-use so two runs never grab the same one — no more flaky tests fighting over the same account.
Every run is triaged, trended, and explained, so you know what broke, whether it's new, and exactly where to look.
Trigger runs from GitHub on every pull request and deploy — Otto posts the verdict back as a native status check and can open an issue when something breaks. Any CI can also trigger runs through the API.
Send results to the Slack channels you choose, routed by app and event. Scheduled and recurring runs keep coverage fresh without anyone pressing go.
The controls a CISO asks for, and the deployment options a regulated team needs.
Organize work by team and environment, with role-based access across your whole organization.
Every meaningful action is recorded with who, what, and when — for the reviews that matter.
SSO and SAML so access follows your existing identity provider and offboarding.
Run Otto as a dedicated instance, or inside your own cloud, when that's what compliance requires.
Your providers, your keys, your private endpoint — so your quality platform never dictates your AI strategy.
On a self-hosted deployment, Otto and its data run inside your environment — and a private model keeps your prompts there too.
Free for 14 days. No credit card. Or talk to us about a dedicated deployment.