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Only if the system kept the evidence that formed the decision. A financial agent can explain a past decision from what it stored at the time. It cannot recover inputs, versions, or tool responses it never recorded, and rerunning it today will not reliably produce the same result.
“Replay” covers two different operations. Historical reconstruction rebuilds what the deployed agent received, which versions and rules were active, what its tools returned, and what it finally did. Re-execution reruns the agent, or parts of it, against the models and services that exist today. The first depends on how well the evidence was designed in. The second depends on how many of the original dependencies you can pin, and it often produces a different outcome.
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Two meanings of replay
Historical reconstruction
Historical reconstruction answers four questions: what the system saw, which versions and rules governed it, what each tool did, and what it decided and who intervened. It works from stored evidence and does not need any live service to be running. Its quality is set by the completeness and integrity of the evidence bundle described later in this article.
Re-execution
Re-execution asks a different question: what would this agent do now, given the same input? The answer can change for reasons outside your control, including a new model version, updated reference data, or altered tool behavior. Treat a rerun as a test of current behavior, not as proof of what happened.
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| Question | Historical reconstruction | Re-execution |
|---|---|---|
| Main question answered | What did the system see and do at the time? | What would the system do now with this input? |
| Needs live services | No, if tool responses were stored | Yes, unless tools are replaced by recorded responses |
| Model identity | The recorded identifier and deployment ID | Whatever model is available at rerun time |
| Typical use | Explaining a recorded decision to reviewers or auditors | Testing a new model, prompt, or policy against past inputs |
Label every replay in your own reports with the mode used. A reconstruction should say which stored records it relied on. A re-execution should name the model version, data versions, and tool mode it ran against, and list the differences from the original run.
A hypothetical decision event
The following event is hypothetical and does not describe a real institution. It shows what has to exist for a reviewer to answer questions about it later.
A payments agent screens an outbound wire of 48,000 USD. On 7 October 2026:
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- 14:02:11.384 UTC. The payment-initiation service sends instruction WI-20417 to the agent. The agent assigns event ID evt-7f3a and carries the upstream correlation ID for the wire.
- 14:02:11.902 UTC. The agent loads customer profile snapshot ps-2026-0931, version 27, from a versioned store. The stored record’s hash is written to the event.
- 14:02:12.410 UTC. The agent calls screening tool v3.2 with the beneficiary name. The tool returns a match score of 0.71, reference SCR-55120, and list version L-2026-10-07. The tool response is stored in full.
- 14:02:12.655 UTC. The agent applies policy set wire-hold-policy 2026.02, identified by its hash. That policy sets a hold threshold of 0.65. The model deployment, dep-44, records the vendor model version in use.
- 14:02:13.120 UTC. The agent decides HOLD and routes the case to analyst queue Q-AML-2.
- 14:19:40 UTC. Analyst E-1187 clears the hold, selects a reason code, and the override is recorded against evt-7f3a.
From this trail, a reviewer can establish that the score exceeded the threshold, which list version produced the match, which policy text was in force, and that a person overrode the outcome. A rerun today would likely differ on the list version and possibly on the model, because both have moved on. The trail still cannot explain why the model weighted the name as it did, unless the system stored a rationale output it actually generated. A generated explanation is evidence of what the system produced, not a readout of its internal computation.
The evidence bundle
The table below is an editorial design pattern for one decision event. It draws on recordkeeping and auditability guidance and is not a universal required field list. Each row names what to store and what it lets you answer in the example above.
| Evidence element | What to store | What it answers |
|---|---|---|
| Identity and linkage | Stable event ID, correlation ID, parent ID | Which upstream wire and downstream analyst action belong to this decision |
| Time | UTC timestamps, clock source, and synchronization status | The order of events, and whether timestamps can be trusted to the second |
| Actors | Agent instance, calling service, human reviewer identity | Who or what acted at each step |
| Input | Request payload or a controlled reference to a stored snapshot, with its hash | What the agent actually received |
| Data sources | Identifiers and versions of reference data, such as list L-2026-10-07 | Which external data existed at decision time |
| Model and deployment | Provider, model version, deployment ID | Which model ran, not which model is current |
| Instructions and rules | Hashes of prompts, policies, and configuration, plus the retained text | Which rules were active; a hash alone proves nothing without the stored text |
| State transitions | Each agent state change with timestamp | How the agent moved from intake to decision |
| Tool calls | Tool name, arguments, full results, errors, external response IDs | What each tool did and returned |
| Outputs | Intermediate and final outputs | What the agent produced along the way |
| Decision and thresholds | Action taken, plus the thresholds and confidence values the system actually used | Why the case was held, and whether the score cleared the line |
| Human involvement | Approvals, overrides, escalations, reason codes | Whether a person changed the outcome, and on what basis |
| Amendments and access | Tamper-evident record of changes, deletions, and reads | Whether the bundle is complete and who has seen it |
Store the personal data referenced by the bundle separately from the trail. The trail can then be shared with reviewers while customer data follows its own access and retention rules.
Keep the tamper-evident trail and the replay package separate
A tamper-evident event trail shows that a record has not been silently altered. A replayable evidence package holds what is needed to reconstruct the decision. Each can fail without the other.
- Immutable but incomplete. The log records “tool called, result received” but not the result body. The trail is unchanged, and the decision cannot be reconstructed.
- Complete but unverifiable. A full bundle is copied into a folder with no hashes, no entry sequence, and no access log. Reviewers cannot show it is the bundle that ran.
Designing for both means storing artifacts in a versioned store and referencing them by hash, chaining or signing the event entries so gaps and edits are detectable, and recording every amendment, deletion, and access as its own event.
Storage, amendments, and change history
The U.S. broker-dealer rules offer a concrete model for handling change history, even though they are not AI-specific. SEC staff guidance on Rule 17a-4 describes two options for covered electronic records: a write-once, read-many (WORM) approach, or an audit-trail alternative. Both are recognized. Neither is categorically better.
| Dimension | WORM approach | Audit-trail alternative |
|---|---|---|
| Main control | Prevents the stored record from being rewritten or deleted | Permits changes but records them, with timestamps and the identity of the person where applicable |
| Reconstruction of original | The original is kept as written | The system must preserve information needed to recreate the original record |
| Change and deletion records | Not the primary mechanism | Must record changes and deletions |
| Usability and access | Check the guidance for reasonably usable electronic production and independent access, including specified cloud-provider arrangements | Same guidance applies to both options |
For an AI decision bundle, the audit-trail model maps naturally onto amendments: a corrected field is allowed, but the old value, the new value, the time, and the person are all kept. Pair it with the following operating decisions:
- Retention. Set periods per the scope rules in the next section, and apply them to the trail, the artifacts, and the personal data separately.
- Access control. Separate who can write events, who can amend them, and who can read them. Log every read.
- Privacy minimization. Tokenize customer identifiers in the trail and keep the mapping in a restricted vault.
- Export. Use a documented, non-proprietary format and confirm that a reviewer without system access can read it.
- Restore. Periodically restore a bundle into a clean environment and confirm the hashes and sequence still validate.
- Key management. Sign batches with keys held in a key management service, plan rotation, and retain the verification material alongside the logs so signatures can still be checked after keys change.
Why reruns diverge from the original decision
Re-execution is useful for testing, but it will not reproduce a historical decision on its own. Common reasons for divergence include:
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- Updated reference data, such as a screening list published after the original run.
- Changed tool behavior, including responses from external services that differ on a second call.
- Changed prompts, policies, or configuration that were not pinned by hash.
- Generation settings that allow variation between runs.
Do not describe a rerun as bit-for-bit reproducible unless your implementation demonstrates that. A practical middle path is to replay the agent’s logic against recorded tool responses. That isolates changes in the agent itself from changes in external services, and it should be labeled as a re-execution with recorded tools. Public sources do not quantify how often financial-agent decisions can be replayed successfully, so any figure you encounter should be checked against its method.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Mapping the design to regulatory scope
Requirements depend on jurisdiction, the organization’s role, the record type, and whether the system falls in a regulated category. A payments agent may be out of scope of every instrument below, or in scope of several. Work through scope first, then select retention and format.
EU AI Act (Regulation (EU) 2024/1689)
The consolidated text dated 27 July 2026 says high-risk AI systems must technically allow automatic event logging over their lifetime. Logs should capture events relevant to risk identification, post-market monitoring, and deployer monitoring. The Act sets a baseline of at least six months for certain automatically generated logs, subject to applicable Union or national law and data-protection law. It gives special documentation treatment to financial institutions subject to relevant EU financial-services governance rules. Its ten-year period applies to specified provider technical and quality-system documentation, not to log retention generally.
These duties are conditional. First establish whether the system is high-risk, whether your organization acts as provider or deployer, and which obligations attach to that role.
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The guidance covers broker-dealer covered electronic records. Its amendment took effect on 3 January 2023, with a compliance date of 3 May 2023. Under the audit-trail alternative, the system must preserve a complete, time-stamped trail that records changes and deletions, timestamps relevant actions, identifies the person where applicable, and preserves information needed to recreate the original record and support its authenticity and reliability. This is a recordkeeping example for one regulated activity. It is not an AI-specific rule.
NIST AI Risk Management Framework
NIST describes the AI RMF as voluntary, designed to incorporate trustworthiness into the design, development, use, and evaluation of AI. NIST’s official page states that AI RMF 1.0 is being revised, so confirm the current version before citing it in a control document.
Financial Stability Board consultation, 10 June 2026
The FSB consultation report proposes 12 sound practices for organization-wide AI governance and lifecycle management in financial institutions, and asks whether they address generative and agentic AI. It is a consultation, not a final or binding standard. Its opening states: “Financial institutions are leveraging AI to transform operations and services, but its rapid adoption may also amplify or introduce risks that need to be identified and managed appropriately.” Check for a later FSB report before treating the practices as settled.
NIST-hosted paper on internal algorithmic auditing
The paper describes documentation and auditability problems in iterative AI development and proposes an audit workflow: Scoping, Mapping, Artifact Collection, Testing, and Reflection (SMACTR). Use it as a workflow reference for organizing artifacts. It is not a regulatory standard.
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Testing a design against a real closed event is more informative than reviewing the schema alone. Use a past decision where the outcome is already known.
- Pick a closed event. Choose a decision with a human override or a clear outcome, and confirm that every linked ID resolves to a record.
- Verify integrity. Recompute the hashes of stored prompts, policies, configurations, and input snapshots and compare them with the recorded values. Verify the chain or signatures from the first entry to the last.
- Check completeness. Compare the tool calls in the agent trail with the tool gateway’s own logs. Flag any call present in one and missing in the other.
- Rebuild the timeline. Order events by UTC time, note the clock source for each service, and flag any sequence that depends on timestamp skew.
- Write the historical reconstruction. Describe the decision using only stored evidence. Mark any field the bundle does not contain as “not recorded” rather than inferring it.
- Run a labeled re-execution. In an isolated environment, pin the recorded model version if it is still available, replay the input snapshot against recorded tool responses, and compare outputs. Record every difference and its cause.
- Test export and access. Export the bundle in its documented format and have a reviewer without system access read it, confirm the hashes, and identify the timeline.
- Test an amendment. Correct one field in a test copy and confirm that the old value, new value, time, person, and reason are all recorded, and that the original remains recoverable.
A failed step is useful information. A gap found in step three or a missing field found in step five points directly at the logging change that would close it.
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