A verifiable audit trail for AI agent actions

Turn every high-stakes thing your agents do into a signed, tamper-evident record you can hand an auditor — not a log line you hope nobody edited.

The problem

When an AI agent takes a consequential action, the question that follows is simple: who approved this, and can you prove it? Most setups answer with application logs — lines that live inside a runtime, can be edited like any other text, and vanish the day you switch frameworks. That is fine until a payout is disputed, a regulator asks, or a customer challenges a decision. At that moment an unsigned log is not evidence; it is a claim. Teams in finance, legal and compliance need a record that stands on its own.

How Raposa solves it

Raposa records every approval decision in a hash-chained audit trail. Each entry captures who approved or rejected, when, and on what grounds, and each is linked cryptographically to the one before it. Because the entries form a chain, altering a single past record breaks verification from that point forward — tampering becomes detectable instead of silent. The trail is signed and exportable, so it can be verified independently of the agent that produced it, and it does not disappear when you change agent frameworks.

The audit trail is a by-product of normal operation, not extra work. When your agent calls request_human_approval() and a person decides, the decision is sealed automatically. Named approver groups and N-of-M sign-off mean every signer is on the record; reminders, escalation and expiry are audited too. Data stays in the EU and a DPA is available, so the same record that gives you operational confidence also satisfies auditors and data-protection requirements.

What it looks like

An HMAC-signed webhook can push each sealed decision into your own systems, so your data warehouse or ticketing tool holds the same provable record.