Why won’t your October agent behave like your January one?
In brief.
An AI agent built in-house relies 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 tests in most in-house projects, and you get silent drift, invisible until the incident. Yet an audited process demands the opposite: reproducibility.
An inheritance imposed, not chosen
In conventional software, code only changes when you change it. An agent built on an external model interface, by contrast, inherits every change the provider makes: new versions, behavioural adjustments, modified default settings. Some changes improve your results; others degrade them on your specific cases. Without a test bench that is replayed regularly, nobody notices: the answers remain fluent and confident, and only their accuracy shifts.
Reproducibility, an audit requirement
For an audited process, being able to replay the processing of a file and get the same result, or explain the difference, is a basic requirement. A set of prompts stacked on a remote model offers neither. Yet that is exactly what an auditor or a key-account customer will ask: how was this answer produced, and would we get the same one today? The countermeasures (freezing and versioning models, logging, replaying a regression test bench, documenting discrepancies) are an ongoing job for a software vendor, not a project delivered once.
The Optivalue.ai approach
Optivalue.ai controls its models end to end: controlled versions, private deployment per customer, full logging of processing. The system’s behaviour is a managed parameter, not a variable you are subject to.
Why do the answers change when nobody has touched anything?
Because the underlying model, hosted by its provider, has changed; the agent inherits the change silently.
How can drift be detected?
By regularly replaying a fixed test bench and tracking the accuracy curves.
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