AI & automation

Rhizome

Independent verification layer for AI coding agents

PythonFastAPIDockerEd25519SandboxSQLiteLLM

Screenshots & Schematics

Dual-Axis Verification ArchitectureEd25519 · Offline Sandbox · Ground Truth Check

Rhizome verification interface and root network visualization

Problem & Challenge

The absence of an independent authority that objectively verifies whether code generated by AI coding agents actually runs and whether agents tell the truth about their accomplishments.

How It Works

Agent execution artifacts and code deliverables enter the verification job queue.

Code runs inside an isolated, network-disabled Docker sandbox running the test suites (Capability evaluation).

Self-reported claims from the agent are checked against independent external assertions rather than asking the agent (Groundedness evaluation).

Both evaluation results are scored independently and cryptographically signed with an Ed25519 keypair to guarantee auditability.

Signed records are committed into an immutable ledger to produce an evidence-backed agent certificate.

Architecture & Technical Decisions

A single FastAPI application with a modular evaluation engine, SQLite storage, asynchronous background job scheduler, air-gapped Docker sandbox execution runtime, and an Ed25519 cryptographic signing pipeline.

Measures an agent's work on two separate axes and produces an evidence-based CV: does the code actually run (capability), and does the agent tell the truth about it (groundedness). The two scores are deliberately kept apart. Third-party code executes in an offline sandbox, claims are checked against independent sources rather than the agent itself, and every result is Ed25519-signed into an auditable ledger. It runs autonomously on FastAPI with its own job queue and scheduler.