TEMTRACE

Temporary review draft. Engagement scope was supplied by VurtuoLabs. The implementation details, before-and-after narrative, and numerical targets below are illustrative—not verified client results—and require approval before publication.

Making AI activity independently inspectable

TEMTRACE’s published offering emphasizes digital evidence and cryptographic notarization. The engagement brief focused on connecting Agentforce activity to that evidence model through hashing. The aim was to make selected AI interactions and actions traceable to a recorded, verifiable representation—not to claim that a hash proves an AI response is correct.

This draft describes an evidence integration around a defined set of events. It separates three questions that are often blurred together: what the agent did, what information was recorded about it, and whether that record has changed since it was captured.

The starting point: a gap between an action and its evidence

In the illustrative starting state, reviewing an AI-assisted workflow meant reconstructing events from conversation history, application records, and integration logs. Those sources could explain different parts of the process without providing one stable identifier or a consistent verification method. Missing events, retries, and changes in payload format made that reconstruction harder.

The technical problem was to preserve useful context without collecting everything indiscriminately. An evidence record needs enough information to identify and verify an event, while respecting access restrictions, sensitive content, retention rules, and the operational limits of the source platform.

What we did: an evidence pipeline with explicit boundaries

Defined the event contract

We started by defining which Agentforce events were eligible for capture and what each record needed to contain. The proposed contract included a stable event identifier, correlation identifier, event type, timestamps, schema version, and approved references to the underlying business context. Capture points were selected according to the interfaces actually available in the implementation, rather than assuming every internal model step could be observed.

Normalized records before hashing

The integration design normalized the approved payload before calculating its cryptographic digest. Deterministic serialization matters: equivalent records should not produce different hashes because field order or formatting changed. Schema and hashing versions were recorded alongside the digest so a later verifier could reproduce the same calculation. Sensitive data was minimized before capture; hashing alone was not treated as anonymization or encryption.

Linked capture, receipt, and verification

The proposed processing path accepted the event, validated it, generated its digest, and submitted the evidence record through TEMTRACE’s supported interface. A returned receipt was linked to the event and source reference. An independent verification step then recalculated the digest from the authorized record and compared it with the recorded value. Missing records, unavailable receipts, and mismatched digests were presented as different states—not collapsed into a generic success or failure.

Designed for retries and operational ownership

The delivery model included idempotency controls to prevent duplicate evidence from ordinary retries, a retry policy for transient failures, and a recoverable exception queue for events requiring intervention. Monitoring tracked capture coverage, processing latency, queue age, and verification failures. Permissions separated event submission from evidence review, while a runbook defined who could investigate, retry, or escalate a failed record.

The delivery package

The proposed handover comprises an event catalog, versioned payload schema, Agentforce integration mapping, canonicalization and hashing specification, receipt-linking workflow, verification utility, failure-handling tests, access matrix, and operations runbook. Test cases cover a valid record, a changed record, duplicate submission, unavailable downstream service, and a failed verification attempt.

Success measures: illustrative evidence targets

100% coverage of eligible events

An example completeness target: every event in the agreed capture scope is accounted for with a verifiable receipt or an explicitly tracked failure awaiting resolution. Successful receipt coverage must also be reported separately, so an exception queue cannot disguise missing evidence. This target does not imply capture of all AI activity across every system.

Receipt generation in less than 5 seconds

A proposed latency target at the 95th percentile, measured from accepted event submission to receipt availability under an agreed test load. The evaluation should report throughput, payload size, errors, and dependency delays alongside latency. This is an illustrative acceptance target, not an observed production benchmark.

The operational transformation

The intended shift is from reconstructing a workflow after the fact to following a defined evidence chain. Reviewers can identify a captured event, locate its receipt, and run the corresponding integrity check. Engineers can distinguish a source issue from an integration or verification failure. The value is a clearer, testable account of recorded activity—not a blanket claim of AI accuracy, legal compliance, or absolute immutability.

Before publication, confirm the actual event interfaces, hashing method, storage and trust model, receipt behavior, load-test results, retention approach, and deployment status. Tamper resistance depends on those controls as well as the digest itself.

Background and reference reading

These sources inform company context and editorial structure only. They do not verify VurtuoLabs delivery details or results.

Company background: TEMTRACE services

Technical reference: Salesforce Engineering on Agentforce auditing