RuntimeVerify | Multi-Agent Security Operations Center

Swarm Fleets

4 Monitored entities

Total Telemetry

0 Canonical events

Verified Safe

0 Passed gating

Human Signoff

0 Awaiting signoff

Interventions

0 Critical blocks

Statistical Drift

0.0% SPRT anomaly rate

Wald Sequential Probability Ratio Test (SPRT)

Cumulative log-likelihood ratio $S_n$ monitored against boundary thresholds

Upper A: +2.944 Lower B: -2.944

Hypothesis: H0 (Compliant Operations)

Gating Verdict Breakdown

Real-time

Sequential Engine: ✓ Active

Deterministic Engine: ✓ Zero-Bypass Enforced

Audit Ledger: ✓ SHA-256 Chained

High-Risk Interventions & Blocked Threats

0 logged

No critical interventions recorded. All actions verified safe or pending signoff.

Phase 17 Verified

Multi-Agent Swarm Topology DAG

Topological mapping of agent delegation channels, privilege tiers, and cross-agent anomaly detection

TOPOLOGY PIPELINE: planner → coder → tester → executor • 4 Nodes Active

Click any agent node to inspect permissions & Markov baseline. Baseline Topology: Strict Linear Pipeline

Agent Node Inspector

Tier P1

Agent Identity: planner-agent

Assigned Role: Architecture Planner (Lead Coordinator)

Parent Hierarchy: Root [No Parent]

Permitted Delegation Targets:

coder-agent

Markov Baseline State: PLAN → DELEGATE (Prob: 0.98)

Security Invariant:

Lead planner cannot execute destructive tools directly; all code modifications must flow through coder & tester.

Mathematical Rigor

Wald SPRT Drift Workbench

Sequential Hypothesis Testing with Markov transition log-likelihood evaluation

Real-Time Phase Space Trajectory Sequential sampling boundaries derived from Wald (1945) formulation

Current LLR ($S_n$) 0.000

Lower Boundary (B) -2.944 Accept $H_0$ (Compliant)

Current Zone SAMPLING Continue Monitoring

Upper Boundary (A) +2.944 Reject $H_0$ (Drift Detected)

SPRT Formal Specification

Cumulative LLR Equation:

$$S_n = \sum_{i=1}^n \ln \left(\frac{P(x_i \mid H_1)}{P(x_i \mid H_0)}\right)$$

Wald Boundary Formulations:

• $A \approx \ln\left(\frac{1 - \beta}{\alpha}\right) = \ln(0.95 / 0.05) \approx +2.944$

• $B \approx \ln\left(\frac{\beta}{1 - \alpha}\right) = \ln(0.05 / 0.95) \approx -2.944$

Optimal Stopping Time (Wald's Lemma):

Minimizes the expected sample size $E[N]$ among all tests with significance $\alpha$ and power $1 - \beta$. Early exits trigger on the very first anomalous sequence.

Current State Trajectory:

• Initial state: compliant baseline (LLR = 0.000)

Autonomous Agent Fleet

Monitored agent entities, behavioral baselines, and session trajectories

Agent Identity	Sessions	Total Events	Interventions	Baseline Model	Last Active	Actions

Canonical Telemetry Stream

Chronological event timeline with structured parameters and decision mapping

Timestamp	Event ID	Agent	Type	Target / Resource	Verification State	Inspect

Evaluated Decisions Log

Deterministic policy checks, semantic classification, and behavioral SPRT evaluations

Timestamp	Action Target	Agent	Verdict	Risk Tier	Confidence	Latency	Details

Dual-Control Human Authorization Queue

High-risk actions paused in REVIEW state requiring dual-control signoff

Request ID	Agent & Session	Action Target	Risk Level	Reason	Status	Human Verdict

Deterministic Security Policies

Pre-execution gating rules, syntax validation, and match simulation

Active Loaded Policy Rules

0 rules active

Rule ID	Domain	Severity	Decision	Description

Policy Syntax Validator

Validates against Pydantic schema v1.0 specifications.

Tamper-Evident

Cryptographically Verifiable Audit Ledger

SHA-256 chained hash integrity, correlation tracking, and zero-leakage secret redaction

Timestamp	Record Type	Severity	Correlation (Trace / Session)	Summary	Secret Redacted	Inspect