Versioned Decision Preview Traces
Each tested scenario records the proposed action, evidence, applicable rule, expected outcome, and reason.
Audit and compliance
AI Agent Auditability and Compliance
A log shows that an action happened. Rippletide explains why it could continue, needed a person, or had to stop, using the business evidence and rule behind the decision. Your team can review the decision without reconstructing it from scattered records.
The auditability crisis
As AI regulation accelerates, enterprises deploying autonomous agents face an auditability gap that existing architectures cannot close.
- AI governance reviews can ask for explainability and human oversight
- Security reviews can ask for evidence that controls operate as designed
- Most agent architectures produce outputs but not decision evidence
- Without structured traces, review becomes a manual reconstruction exercise
How Rippletide prepares audit evidence
Rippletide links a proposed business action to the evidence, applicable rule, outcome, and reason. A reviewer can see which conditions supported the decision and which evidence was missing or conflicting.
The 10-Day Proof evaluates one agreed action on representative cases without changing production. It checks whether the decision and its explanation meet the agreed criteria. Production activation of the same action is scoped separately.
Every Rippletide decision is fully explainable: evidence, applicable rule, outcome, and reason. The explanation is the decision path, not a post-hoc model summary.
Policy Conformance Records
Each preview records which explicit rule passed or failed. The record supports review without claiming legal compliance.
Review-Ready Evidence
A structured dossier helps security, risk, and production owners review the proposed control boundary. It does not replace their compliance assessment.
Questions enterprise reviewers ask
The exact requirement depends on the company and jurisdiction, but sensitive agent workflows repeatedly raise the same evidence questions.
- Action: What exactly can the agent change?
- Evidence: Which records must be present and consistent?
- Approval: Who owns exceptions and thresholds?
- Trace: Can the reviewer reconstruct the expected decision?
What an audit-ready decision looks like
Picture a finance operations agent reviewing whether a dossier can move to the next workflow state. An offline decision preview produces a trace that contains:
- Action. Advance finance dossier 7821 to review-ready status.
- Facts evaluated. Required signer data is missing and one supporting document contradicts the contract type.
- Policies evaluated. Dossier completeness and exception rules, with their versions.
- Preview outcome. Escalate for correction before the workflow state changes.
- Trace. Links to the source records, rule, outcome, and reason.
Six months later, a reviewer can see why the dossier was held without reconstructing the decision from unstructured logs.
From audit theatre to audit by design
Most AI agent stacks bolt audit on at the end through logs and periodic exports. The 10-Day Proof first proves that the evidence and rule behind a risky action can be reconstructed consistently.
- Tested scenarios include the evidence and policy version used.
- Unsafe cases are documented instead of hidden in aggregate scores.
- The boundary remains outside the model prompt.
Frequently asked questions
Does Rippletide provide full decision explainability?
Yes. Every Rippletide decision is fully explainable: evidence, applicable rule, outcome, and reason. The explanation comes from the decision path itself, not a post-hoc model summary.
What does a decision record help a reviewer understand?
The proposed action, the evidence and rule used, the outcome, and the reason. For an offline decision preview, the record describes the tested scenario. It is not proof that an action ran in production.
Are AI agent logs enough to satisfy SOC 2 or the EU AI Act?
Logs show events, but reviewers may also ask which evidence and rule supported an action. Rippletide packages those links in a decision preview trace. Customers determine which controls meet their applicable requirements.
Can the audit trail be modified after the fact?
The current 10-Day Proof produces versioned traces from offline scenarios and representative records. Tamper-evident runtime evidence belongs to the enterprise Runtime foundation.
How does this fit with our existing observability stack?
Rippletide complements observability. Event logs show what happened. Rippletide explains the business decision: which evidence and rule led to Continue, Needs a person, or Must stop. An offline decision preview shows the result for the tested scenario, not a live production event.
Does this slow agents down?
The first 10-Day Proof scope runs offline on scenarios and representative traces, so it adds no latency to the live agent. Runtime latency is measured on the customer workload during expansion.
Learn more
See how AI agent governance provides the policy foundation for auditability. Explore agent decision infrastructure to understand the path from decision previews to Runtime. Learn how enterprise AI guardrails differ from an explicit business action boundary.
Free Risk Review
Make one risky write-action reviewable
Map the action, evidence, policy, and approval gaps in one 30-minute working session. No live production access required.
- Evidence linked to each decision preview
- Policy and scenario versions recorded
- Security review dossier structure