Run agents in parallel, evaluate outputs against versioned policies, and emit cryptographic receipts before any code is merged.
Logs aren't enough when AI agents write critical code in parallel.
Decisions become invisible: there's no trace connecting intent to output.
Without evidence or reproducibility, you cannot audit agentic action safely.
Sibar formalizes the chain between execution and acceptance using an immutable ledger and deterministic evaluation.
Define invariants and safety bounds in your repo.
Immutable log of agent traces, inputs, and artifacts.
Verifiable proof of policy adherence before merge.
Run multiple agent models on the same task. Compare their outputs with hard evidence, not vibes.
Re-execute traces with the same inputs and policies to detect non-determinism during audit.
Every accepted PR carries a cryptographically signed receipt showing what changed and why.
Direct onboarding for early engineering teams.