Government AI Code Testing.
Public-sector engineering teams adopting AI coding agents face a procurement reality: open-source preferred, no telemetry, no SaaS-vendor lock-in, local execution mandatory. tailtest's open-source MIT plugin is built around all four of those constraints from day one. FedRAMP-style audit-evidence packaging is on the roadmap.
Why tailtest fits the public-sector procurement profile
- Open source (MIT). Auditable code. No vendor lock-in. Self-host trivially.
- No SaaS dependency. No remote endpoints called by tailtest itself. Whatever your AI coding tool (Claude Code, Cursor, Codex, Cline) does over the network is independent of tailtest.
- No telemetry. tailtest does not phone home. No usage analytics, no error reporting back to us, no opt-out toggle to forget. The architecture excludes the surface.
- Local-only execution. Tests run on the same machine as your AI tool. Test results stay in
.tailtest/session.jsonand your test runner's output. - 10 languages supported. Including Java (common in legacy public-sector systems), Python (common in data tooling), Go (common in modernization projects).
On the roadmap (for public-sector context)
Q4 2026: Audit-evidence packaging
Session logs aggregated, signed, packaged for review. Format-agnostic (CSV, JSON, structured PDF) for compliance frameworks that prescribe particular evidence formats.
Q4 2026: OWASP-aligned security testing
Code-level security tests aligned with OWASP Top 10, NIST 800-53 control mappings where feasible.
Exploring: Air-gapped install mode
For environments where outbound internet from the build machine is disallowed. tailtest itself works air-gapped; this is about packaging install for that mode.
Public-sector adoption conversations
Compliance and procurement teams welcome to evaluate against your specific framework.