How Sgraal prevents costly AI agent failures across regulated industries — and what production integration looks like for design partners.
About these case studies: The scenarios below are hypothetical, grounded in real Sgraal capabilities and corpus attack patterns. Detection scenarios illustrate what Sgraal's scoring layers catch; deployment scenarios illustrate what production integration looks like for design partners. No actual client data is represented, and all metrics shown are illustrative of the use case — not real customer outcomes. As actual customers go public, named case studies with verified outcomes will replace these hypotheticals. Currently in pilot deployment with anonymized design partners; see the homepage Design Partner Program.
Part 1 of 2
Single-incident attack patterns from the Sgraal corpus and the scoring layer that catches each.
Hypothetical scenario based on real corpus attack pattern.
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.
Hypothetical scenario based on real corpus attack pattern.
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.
Hypothetical scenario based on real corpus attack pattern.
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.
Part 2 of 2
What production integration looks like for design partners in regulated verticals — endpoints, architecture, and ongoing operational outcomes.
Hypothetical deployment grounded in real Sgraal endpoints (/v1/check, /v1/certify/mvmem, /v1/proofs/convergence).
Industry
Clinical AI
Identity
Fortune 500 health system, CMO office innovation team
Jurisdiction
US + EU (HIPAA + GDPR overlap)
Scale
1 production agent · 3 environments (dev / staging / prod)
Sgraal tier
Tier 1 Live integration
Clinical AI agents drafting patient communication operate at the intersection of HIPAA (US Minimum Necessary Rule) and GDPR (EU Article 5(1)(c) data minimization). Each memory access touches identifiable patient context, and the DPO and Compliance team require a per-decision audit trail — not a quarterly prose summary. Pre-Sgraal: a 3-week manual sign-off cycle per major model change, with reconciliation done in a spreadsheet against access logs.
Endpoints in use:
POST /v1/check on every agent decision — real-time verdictPOST /v1/certify/mvmem on flagged decisions — W3C VC for audit trailPOST /v1/proofs/convergence quarterly — Lyapunov stability documentation for internal model governance reviewArchitecture: Sgraal Proxy mode in front of the internal LLM agent, with a 60-second verdict cache for similar queries. MVMem certificates archived in the existing audit log system; FDA-ready Convergence Proof PDFs stored alongside the internal model registry.
DPO sign-off cycle
3 weeks → 2 days
Audit trail
Per-decision, machine-verifiable
Verdict distribution
~12% WARN · ~3% BLOCK
A team in this position would frame this as: "We replaced 60 hours of monthly compliance reconciliation with 30 minutes of MVMem certificate review."
Hypothetical deployment grounded in real Sgraal endpoints (/v1/check, Edge mode, hosted calibration sync).
Industry
Quantitative finance / AI trading
Identity
Series B fintech, AI execution desk
Jurisdiction
US (SEC + FINRA) + EU markets regulation (trade reporting + best-execution)
Scale
4 production agents · ~10K decisions/day
Sgraal tier
Tier 2 Beta (early access)
AI trading agents act on market memory and order-book history under a sub-15ms latency budget. A stale-memory hallucination at decision time translates directly to execution risk — and post-hoc replay analysis only catches the damage after it's done. The compliance officer needs an explainable audit trail for regulator inquiries, not a black-box agent log. Pre-Sgraal: replay-only governance, no real-time intervention path.
Endpoint in use:
POST /v1/check pre-execution on every order — sub-15ms latency budgetWARN verdicts → human review queue (does not block execution)BLOCK verdicts → halt execution immediately, raise on-callArchitecture: Edge mode (offline scoring) deployed alongside the execution agent for sub-millisecond latency on the hot path; periodic sync to the hosted Sgraal calibration service. The Sgraal verdict score is recorded as a field in the per-trade explainability log alongside risk-management metadata.
Average verdict latency
8 ms (sub-15ms budget met)
Order distribution
~0.3% WARN-flagged for human review
Audit trail
Per-trade verdict snapshot stored with order data
An execution desk lead would frame this as: "Sgraal is the ABS for our agents — it doesn't make us fast, it makes us safe at speed."