Why will your October agent not behave like your January one?

In brief.
An AI agent built in-house rests on models that their providers update continuously: every change alters your system’s behaviour, without notice. Add non-determinism — the same file processed twice can produce two different results — and the absence of control testing in most internal projects, and you get silent drift, invisible until the incident. Yet a process subject to audit demands the opposite: reproducibility.

An inheritance endured, not chosen

In conventional software, code only changes when you change it. An agent built on an external model interface, by contrast, inherits every one of the provider’s changes: new version, behavioural adjustments, modified default settings. Some changes improve your results, others degrade them on your specific cases. Without a test bench replayed regularly, nobody detects it: the answers remain fluent and confident, only their accuracy moves.

Reproducibility, an audit requirement

For a process subject to audit, being able to replay the processing of a file and obtain the same result — or explain the difference — is a baseline requirement. An assembly of instructions on top of a remote model offers neither. Yet that is exactly what an auditor or a major client will ask: how was this answer produced, and would we get the same one today? The countermeasures (pinning and versioning models, logging, replaying a regression bench, documenting deviations) are permanent vendor work, not a project delivered once.

The Optivalue.ai approach

Optivalue.ai controls its models end to end: controlled versions, private deployment per client, full logging of processing. The system’s behaviour is a managed parameter, not a variable to be endured.

Why do answers change when nothing has been touched?
Because the underlying model, hosted by its provider, has changed; the agent inherits that silently.

‍How do you detect drift?
By regularly replaying a fixed test bench and tracking accuracy curves.

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