Skip to main content
Agents change silently. A model gets swapped underneath one, a prompt is injected, data drifts — and nobody notices until it is a headline. Silent-change detection fingerprints each agent’s behaviour from the decision chain and tells you the day it started behaving differently, with no new system of record and no GPU — just statistics over evidence you already have.

​
The behavioural fingerprint

Sakshi computes a fingerprint per agent from its witnessed decisions — the model serving it, its decision and routing mix, its tool use, its decline reasons — and measures how far a recent window has diverged from the window before it. A dimension is only flagged when the shift is both large and statistically significant, so a quiet agent never cries wolf (the same discipline as the Vishwas fairness screen and the evaluation confidence intervals).

​
Declared vs silent

A flagged change is only alarming if it was undeclared. Sakshi correlates each flagged dimension with the governance events that would explain it — a model registered in the agent’s provenance, an autonomy envelope published — and classifies the change:
  • declared — every flagged dimension is explained by a governance act you took (a registered model, a published policy). Expected; no alarm.
  • silent — a flagged dimension has no governance event to explain it. The agent changed and nobody declared why.
A declared model upgrade and a silent swap look identical in the metrics; the difference is whether you told the registry — and Sakshi checks.

​
The signed change report

Persisting a report Ed25519-signs the verdict as a tamper-evident, RBI-mapped artifact (the unauthorised-change obligation). When the verdict is silent, the report also emits an agent_behavior_changed webhook and flags the agent for re-certification — so a silent change automatically sends the agent back through the fit-for-production gate.