Why AI audit logs break when the facts change

Lians, a three-week-old startup, is building a system of record for AI in regulated workflows that preserves decision reconstruction even when underlying facts change. The company argues that standard AI audit logs fail to reproduce original decisions because they log activity without capturing the state of documents, policies, and records at decision time. Lians is seeking design partners in financial research, risk, and compliance to address this challenge.

Most AI agent audit logs can tell you what prompt ran, what model answered, and which tools were called. That is useful until someone asks a harder question six months later: Why did the agent make that decision with the information available at the time? Imagine an agent reviewing a company, customer, or transaction. It retrieves a policy, a filing, an entity record, and a risk rule. Later, the policy is updated, the filing is amended, and the entity record is corrected. If you replay the workflow using today's sources, you are not reproducing the original decision. You are running a new decision against a different world. You have an activity log, not decision reconstruction. Agent systems need to distinguish at least three clocks: A record can be valid in March, corrected in June, and audited in September. The September review may need both the March version that influenced the original decision and the June correction that changed the current truth. Without those distinctions, old facts can resurface as if they are current, or current facts can silently rewrite the past. For a meaningful reconstruction, I would preserve: Logging only an ID is not enough if the document, policy, or record behind that ID can change later. Content hashes help prove integrity, but the original content or a recoverable immutable version still needs to exist. A new fact should not always overwrite an old fact. Correction: The previous record was wrong. Preserve it for audit, but make the correction authoritative for current decisions. Real-world change: Both facts were true at different times. Give each fact a validity interval. Disagreement: Two sources conflict and neither one clearly supersedes the other. Preserve both with provenance and surface the conflict. This structure lets current retrieval avoid stale information while point-in-time retrieval can still reproduce what the system knew earlier. Decision reconstruction is not only an audit requirement. It improves: If an agent cannot explain what evidence it used at decision time, it becomes harder to improve safely. At Lians https://lians.ai , we are building a system of record for AI in regulated workflows. The goal is to reconstruct what an AI system knew, did, and why at the moment a decision was made, even after the underlying facts change. We are three weeks old, pre-1.0, and working with a functioning product. We are looking for 5 to 7 design partners in financial research, risk, compliance, and other evidence-heavy agent workflows. If you operate an agent where policies, records, filings, or external sources change over time, I would genuinely value hearing how you handle reconstruction today. What breaks first in your current audit trail when the facts change?