What Is AI Agent Audit Trail?
An AI agent audit trail is a complete, tamper-evident record of every action an agent takes: which agent acted, what it did, when, where, why, and how. To count as evidence, it must be captured outside the agent's runtime, hash-chained so any alteration is detectable, and independent of the model provider whose behavior it records.
Who needs this
Deployers of agents in regulated contexts, where the EU AI Act (Article 12), DORA (Article 30), and GDPR (Article 22) each demand reconstructable records. Also anyone who will one day face an incident: when an agent is compromised or misbehaves, the audit trail is the difference between a forensic investigation and an educated guess.
Why vendor logs do not qualify
Every model provider offers usage logs. They fail the audit-trail test on independence: the log lives on the provider's infrastructure, under the provider's keys, shaped by the provider's interests. In any dispute involving that provider's model, its own log is not neutral evidence. Independence from the system being logged is the property, not who holds the pen.
Tamper-evidence is the second test. A log a database administrator can edit is a report, not a record. Hash-chaining fixes this structurally: every entry carries a hash of its predecessor, so altering any past entry breaks every subsequent link, detectably and mathematically.
Data point · one ledger entry in the Kyde Gateway
entry_id unique identifier of this action
timestamp millisecond-precise capture time (WHEN)
agent_id cryptographic agent identity (WHO)
action request type and target (WHAT / WHERE)
why causal context captured before the call (WHY)
tool_calls tools invoked and their arguments (HOW)
model provider and model version
prev_hash hash of the previous entry ─┐
entry_hash hash of this entry ─┴─ the chain
signature Ed25519, on the Enterprise editionSix dimensions per action: who, what, when, where, why, how. Every entry is hash-chained; Ed25519 signing and managed keys are Enterprise.
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