Latency, reliability, and architecture of the Sgraal preflight pipeline.
Last updated: April 2026
12ms
p50 latency
23ms
p95 latency
41ms
p99 latency
99.97%
Uptime (Q1 2026)
83 sequential scoring modules evaluated per preflight call. Pipeline includes Weibull freshness decay, 5-method drift ensemble, sheaf cohomology, Shapley attribution, Z3 formal verification, and 4 post-reconciliation detection layers. All modules execute in a single synchronous pass.
Detects timestamp forgery: old decisions disguised as fresh. Content-age mismatch, fleet age collapse, anchor inconsistency.
Detects gradual authority escalation across agent hops. Subject rebinding, confirmation erosion, permission lattice violation.
Detects self-reinforcing false consensus from a single root source. Hedge marker decay, confidence recycling, cross-role reinforcement.
Detects circular references, chain length mismatches, and compromised agents in the memory provenance path.
When multiple detection layers fire simultaneously, Sgraal computes a unified attack surface score: r1 + 0.3×r2 + 0.1×r3 + 0.05×r4. Levels: NONE, LOW, MODERATE, HIGH, CRITICAL.
3 consecutive failures trip the breaker. 30-second recovery window. Prevents cascading failures from upstream dependencies.
Graceful degradation when Upstash Redis is unavailable. Core scoring continues without stateful features. Demo keys are fully stateless by design.
Same input produces same output, every time, on any machine. SHA256-seeded stochastic modules. Hysteresis suppresses jitter < 3.0.
Railway auto-deploy from main branch. Rolling restarts with health checks. No manual intervention required.
614
Test cases across 8 rounds
0
False negatives
97.2%
Adversarial detection (216 compound cases)
Automated calibration loop monitors threshold drift and classifies mismatches as corpus_wrong, threshold_wrong, or ambiguous.