How Sgraal prevents costly AI agent failures across regulated industries.
An algorithmic trading agent prepared to execute a large position based on a memory entry referencing a 2024 SEC ruling. The entry had timestamp_age_days: 0 — presenting stale regulatory data presented as current.
Sgraal's timestamp integrity layer detected content-age mismatch: the content referenced "Q2 2024" and "deprecated framework" while claiming to be freshly created. Result: timestamp_integrity: MANIPULATED.
A medical triage AI gradually expanded its diagnostic authority across 8 agent hops. By hop 6, it was recommending treatments outside its authorized scope — each individual hop looked plausible.
Sgraal's identity drift layer detected authority expansion keywords accumulating across the chain: "elevated to", "authorized to execute", "standing authority". Result: identity_drift: MANIPULATED at hop 4.
Three independent research agents all confirmed a fabricated case citation as "verified precedent." No single agent had conflicting information — the consensus appeared genuine.
Sgraal's consensus collapse layer detected self-reinforcing agreement from a single root source. All three entries had near-identical trust scores with zero conflict — statistically implausible. Result: consensus_collapse: MANIPULATED.
These are synthetic scenarios based on real corpus attack patterns. No actual client data is represented.