Your AI writes the code.Who's checking it?

AI agents are writing production code. Copilot, Cursor, Claude, Devin — they generate PRs faster than any team can review them. The bottleneck has moved from writing code to verifying it.

A reviewer opens the diff. 147 lines added across 6 files. They skim the structure, check the import paths, maybe run it locally if they have time. Usually they don’t.

The dev reviews the agent’s plan, skims the generated diff, and hits approve. It looks reasonable. No tests were validated against the spec. No visual regression was checked. No one proved the tests catch real bugs. It felt right.

The PR is merged on opinion alone. No evidence trail exists. If something breaks in three weeks, the team will have to reverse-engineer what happened from git blame and Slack threads.

The bug ships. An edge case in the OAuth refresh token logic causes silent session drops for 12% of users. It takes 3 days to find, 2 days to fix, and costs 847 support tickets. Nobody checked.

$ hyper verify feature/add-oauth --spec "add OAuth2 login" Point Hyper at a branch, PR, directory, or working tree. Give it a spec — even a one-line description of what the change should do. Hyper handles the rest.

Seven deterministic gates run first. Pass or fail, no ambiguity. Install resolves dependencies. Build compiles. Lint checks style. Typecheck validates types. Fast and cheap — most finish in under 2 seconds.

The test gate runs your existing test suite end-to-end. Not a sample, not a subset — the whole thing. If you have 47 tests, all 47 run. This is evidence, not a spot check.

Policy checks enforce your team’s rules: diff size limits, forbidden file paths, naming conventions. The visual gate captures screenshots and compares them pixel-by-pixel against the baseline.

Deterministic gates prove the code is correct. The next gates prove it does what you asked for. This is the gap no CI pipeline fills — verifying behavior against your spec with real execution, not LLM opinions.

Hyper reads your spec and generates targeted test cases. Then it runs them — for real, against your actual codebase. Not "does this code look like it implements OAuth?" but "does calling /auth/login return a valid token with the correct scopes?"

Generated tests are only useful if they actually catch bugs. Hyper injects small, deliberate bugs into the code and checks whether the tests detect them. 87% caught means the tests are real — not just passing, but actually protecting you.

Hyper starts your dev server, opens a real browser, and verifies the spec conditions live. It doesn’t ask an LLM "does this look right?" — it navigates to the login page, clicks the OAuth button, and checks that a token appears. Reality, not inference.

Every gate produces an artifact: logs, screenshots, test reports, mutation results, browser recordings. 11 gates, 11 pieces of evidence. A complete, auditable record of why a change was approved.

All 11 gates produce independent evidence. Hyper combines them into a single confidence score — it’s not an average. Each gate contributes independent evidence, and conflicts between gates increase uncertainty. One number that tells you how much to trust this change.

Confidence 0.91 — well above the auto-merge threshold. The change merges automatically with a full evidence trail attached. No human needed. No reviewer’s time consumed. Only evidence.

$ hyper factory "add OAuth2 login with RBAC" --project . --duration 4h Factory takes a natural-language vision and a time budget. It runs autonomously until the duration expires or the vision is complete.

Factory breaks the vision into discrete specs. Each spec is a single, verifiable unit of work. "Add OAuth2 login with RBAC" becomes: OAuth2 client setup, token persistence layer, RBAC middleware, and integration test suite.

For each spec, Factory spawns multiple agents that compete to produce the best implementation. Three agents might each take a different architectural approach. This isn’t redundancy — it’s selection pressure.

A synthesis step reads all competing implementations and combines the best architecture, tests, and error handling from each into one output. Not "pick the winner" — genuine synthesis of the strongest elements.

Every synthesized change runs through all 11 verification gates. No shortcuts, no exceptions. The same pipeline that verifies human PRs verifies factory output.

Changes that pass with 0.70+ confidence are auto-merged immediately. Between 0.40 and 0.70, changes are merged with monitoring — shipped, but flagged for follow-up. 78.3% of factory output clears auto-merge — most of its work ships without human involvement.

Changes below 0.40 confidence are held for human review. Factory doesn’t guess — it knows when it’s uncertain. A morning review command lets you triage the queue: merge, reject, or inspect with full evidence.

The cycle repeats. Decompose, generate, synthesize, verify, ship. Factory runs overnight, over weekends, over holidays. When you wake up, 2–3 items need your review. Everything else already shipped with evidence.