LedgerRail is governed execution infrastructure for AI agents — every action authorized by policy, every decision auditable, every mistake reversible.
AI agents are moving from read-only assistants to write-access operators — posting journal entries, processing payments, reconciling accounts. But the controls haven't caught up.
AI agents execute actions with no policy check, no approval chain, and no boundary enforcement. If an agent hallucinates an entry, it posts.
When regulators or auditors ask "who approved this?", the answer is a black box. Agent logs aren't audit evidence.
Traditional systems don't track what agents did or how to reverse it. One bad batch post can take days to unwind manually.
LedgerRail sits between your AI agents and your systems of record. Every agent action passes through a governance layer that authorizes, audits, and — when needed — reverses what happened.
Prove who approved what, what happened, and what can be undone — to auditors, regulators, and your board.
Govern AI agents across multiple client books without losing sleep over which agent did what where.
Hash-chained evidence trails that are tamper-detectable and export-ready. Not logs — proof.
Every agent action is tracked as a node in a directed graph. See exactly what caused what, which actions depend on which, and what breaks if something is reversed.
LedgerRail classifies every action as reversible, compensatable, or irreversible — then builds leaf-first undo plans that safely unwind complex chains.
Agents earn trust through demonstrated reliability, not blanket permissions. Wilson score confidence intervals gate each autonomy level: observe, suggest, semi-auto, autonomous.
SHA-256 hash chains per organization. Every action, approval, and reversal is cryptographically linked. Tamper-detectable. Audit-ready.
The governance gap is closing fast. The firms that build trust infrastructure now will lead.
LedgerRail is in early access for finance and accounting teams deploying AI agents into systems of record.