SYSTEMORES

The MCP Bridge acts as a standardized endpoint, permitting developer and production frameworks to access the auditor's functions on-demand.

Target Audience: Prompt engineers, AI designers, and product managers.

When designing system instructions for a new AI assistant, the prompt designer plugs the Systemores MCP server into their local IDE (such as Cursor or Claude Desktop). As they draft guidelines, they ask their coding assistant to audit the prompt. The IDE assistant automatically invokes systemores_stress_test_agent, evaluates the response safety under simulated adversarial pressure, and flags vulnerabilities right inside the editor chat window.

Target Audience: DevOps, security engineers, and compliance officers.

To ensure compliance standards (EEOC hiring acts, ECOA fair lending regulations) are never breached in production, teams integrate MCP calls into their continuous integration (CI/CD) pipelines. On every Git commit or pull request, the test runner starts the Systemores engine to audit the agent's instructions. If the agent's compliance score drops below pre-defined thresholds, the deployment build fails and locks the release.

# Example CI Test Assertion Snippet
const auditResult = await mcpClient.callTool("systemores_assess_bias", {
  systemPrompt: "<your-agent-system-prompt>", // Set via AGENT_SYSTEM_PROMPT env var in CI
  complianceDomain: "employment_eeoc"
});

if (auditResult.status === "FAILED" || auditResult.riskRating === "HIGH") {
  console.error("❌ CI Blocked: Safety checks failed with risk score " + auditResult.metrics.behavioralIntegrity);
  process.exit(1); // Fail build
}

Target Audience: Risk officers, operations leads, and billing administrators.

LLM model weights are constantly updated by model providers (OpenAI, Google, Anthropic). A prompt that works perfectly today might react with high irritability, cost, or drift tomorrow. A recurring cron script can automatically test active production prompts daily, checking systemores_evaluate_drift and systemores_measure_drag. If latency goes up or reliability drops, operations gets notified via Slack or Webhook instantly.

Target Audience: Engineering leads, AI platform teams, and teams building autonomous agents with tool-calling capabilities.

Before deploying an agent that calls external tools — APIs, databases, filesystems, or code interpreters — teams run systemores_tool_integrity to verify the system prompt establishes safe tool-use discipline. The audit checks whether the agent will select the right tools, construct valid arguments, and handle failed or ambiguous tool returns safely without fabricating results. Additionally, it evaluates Source Integrity to measure the agent's groundedness vs. hallucination threshold, checking whether the agent relies on explicit context or falls back to fabricated parametric memory.

# Example: Gate agentic deployment on Tool Integrity score
const integrityResult = await mcpClient.callTool("systemores_tool_integrity", {
  systemPrompt: "<your-agent-system-prompt>",
  toolContext: "database" // api_calls | database | filesystem | code_execution
});

if (integrityResult.status === "FAILED") {
  console.error("❌ Deployment blocked: Tool Integrity risks detected:");
  integrityResult.risks.forEach(r => console.error(" -", r));
  process.exit(1);
}

Target Audience: Enterprise platform architects building multi-agent systems, synthetic population simulators, and regulatory compliance leads.

In complex multi-agent meshes, individual sub-agent prompts may pass isolated tests while the overall execution DAG suffers from privilege escalation, cyclic loops, or token cost traps. Connecting to EthosSim executes full-system red-teaming using 5 multi-agent extensions:

• systemores_audit_agent_graph — Scans DAG execution flows for delegation loops and privilege escalation.
• systemores_generate_cohort — Spawns synthetic user populations with demographic diversity and adversarial inputs.
• systemores_inject_environment_fault — Configures HTTP 429 rate limits, 500 server errors, and timeouts.
• systemores_analyze_trace_telemetry — Ingests OpenTelemetry traces to catch runaway API token burn traps (> $2.50).
• systemores_export_dossier — Exports EU AI Act (EU 2024/1689), NIST AI RMF, and SOC 2 AI dossiers.