Arkna turns fragmented model, tool, data and human activity into a reviewable record of an AI agent run, so your teams can reconstruct what happened, prove it, and account for it without starting from scattered logs.
AI agents are becoming operational across enterprises, approving, denying, scheduling, refunding, escalating. But when something goes wrong, most teams can't replay what happened, prove it, or explain it to a board or a regulator. You find out the agent failed when a customer complains, or when someone with subpoena power asks.
Reads what your stack already emits. Redacts personal data on the wire. Hash-chains every event so the record can't be edited after the fact.
Walk through any agent run frame by frame, verify the hash chain in the browser, and export a packet pre-mapped to EU AI Act Annex IV, ISO 42001, and APRA CPS 230 / 234.
Read what your agents already emit. Drop-in observer for OpenAI Agents, Anthropic, LangGraph, custom stacks.
Strip personal data on the wire. The record proves what happened without exposing who.
Hash-chain end to end. A single altered byte breaks the chain visibly. Tamper-evidence by construction.
Replay any decision, verify the chain in the browser, export the regulator pack. Evidence on demand.
APRA CPS 230 / 234, DORA, OSFI E-23. The audit trail an examiner expects, generated automatically.
PHI-free by default. Replay triage, intake, and prior-auth decisions without exposing patient data.
Hash chains verifiable on air-gapped review systems. Independent by construction.
When the auditor asks for evidence under a specific clause, you click. Arkna already knows which events satisfy it.
A 30-minute review with your risk and engineering leads. We walk through your agent stack and show you what the record would look like.