AI Code Testing for Teams.
Determinism scales; manual review does not.
When five engineers each run AI coding sessions in parallel, you have five branches landing 1,000+ lines each per day. Code review queues balloon. CI catches integration issues days late. Production bugs ship because nobody's read the diff carefully. tailtest enforces the test cycle inside every engineer's AI session deterministically, so the per-PR review surface shrinks back to manageable.
The team-scale problem
When one engineer uses Claude Code or Cursor, "I'll review every line" is plausible. At five engineers, the daily diff volume exceeds what any team lead can usefully read. The natural response -- "we'll trust the AI more" -- works for hours and breaks at month-end when integration bugs surface.
The bugs that survive look like this: each individual diff passes existing tests. Combined, they break user-visible behavior in subtle ways. Manual review doesn't catch them because no individual diff looks wrong. CI doesn't catch them because the tests don't exist yet for the new code paths.
tailtest closes that gap by ensuring every new code path gets test coverage at the time of edit. Per-edit, per-engineer, per-session. The cumulative effect: when a PR lands, the test surface has grown alongside the code surface. Integration bugs surface at edit time, not deploy time.
How teams deploy tailtest
tailtest is a per-engineer plugin. Each engineer installs it in their AI coding tool (Claude Code, Cursor, Codex CLI, Cline). No central server, no team license, no SaaS account. The plugin produces standard test files committed alongside source -- so the team review process stays unchanged. Reviewers see test files in the PR alongside source changes, exactly as they would for human-authored tests.
For team-level consistency, commit a .tailtest/config.json in your repo root. This pins depth (simple / standard / thorough / adversarial) across the team. Engineers' local sessions inherit it.
For monorepos, see the advanced docs -- tailtest handles per-package configuration, accepted-failure baselines, and runner-per-language resolution.
What changes for managers / leads
PR review queues shrink
Test coverage arrives with the code. Reviewers spend time on architecture and logic, not "did anyone write a test for this."
Production bug surface drops
Lightrun 2026 found 43% of AI-generated changes need debugging in production. The bugs that catch tests at edit time don't reach prod.
Adversarial mode for high-stakes code paths
Pin "depth": "adversarial" for payment, auth, data-access, or other high-stakes modules. Boundary + injection + race scenarios fire automatically.
Determinism, not policy
"Write tests for every change" as a policy depends on engineers remembering. As a hook, it fires whether they remember or not. 100% compliance vs the 70-90% prompt-based norm.
No SaaS, no telemetry, no per-seat cost
tailtest is MIT open source. Free for individual use, free for team use, free for enterprise use. No telemetry leaving your machine. No central server tracking what you build. No SaaS account to provision per engineer. The plugin runs entirely local except for whatever calls your AI coding tool already makes.
If you need enterprise features (centralized configuration, audit logs, compliance reporting), see the enterprise solutions page for what's on the roadmap.
Pilot tailtest with one engineer first
Cheapest way to evaluate. One install. One week. See the diff.