SYSTEMORES

The Problem: Support agents are vulnerable to prompt injection, tone hijackings, or jailbreaks where users force them to offer unauthorized discounts, validate illegal actions, or break character.

The Solution: Run the systemores_stress_test_agent diagnostic. It exposes the prompt to adversarial batteries ("Presses") simulating social engineering, hostile demands, and policy overrides. If the agent's Stability or Compliance index drops below thresholds, the deployment is blocked.

The Problem: Automatic decision-making models must comply with fair lending acts (ECOA). An LLM underwriter agent might incorporate subtle, unlawful proxy metrics or demonstrate unintended bias based on system instructions.

The Solution: Execute the systemores_assess_bias tool against the underwriting prompt with compliance domain lending_ecoa. This verifies that demographic attributes remain isolated, underwriting criteria are purely numeric, and decision loops strictly conform to credit standards.

The Problem: Pre-screening recruitment agents can build biased criteria, evaluating candidates using gender, age, or disability proxy keywords.

The Solution: Audit using compliance domain employment_eeoc. The auditor checks the system prompt for hidden selection bias, ensuring candidate evaluations are centered strictly on skills, qualifications, and capabilities, rather than demographic indicators.

The Problem: At temperature values above 0.0, agents might start off adhering to a strict persona but gradually drift away from rules or boundaries as the chat memory grows.

The Solution: Run systemores_evaluate_drift with 20–50 turns and different temperatures. It simulates long-running interactions to track the degradation of traits (like Empathy, Compliance, or Assertiveness) over time, finding the optimal LLM configuration before release.

The Problem: Agents that consume too many tokens per turn or have slow response loops create high operational costs and degrade the user experience.

The Solution: Supply standard token lengths and average delays to systemores_measure_drag. This provides a cognitive efficiency index that measures performance-to-cost ratios, enabling teams to control token burn and reasoning overhead, optimizing inference spend and protecting the bottom line while managing latency budgets.

The Problem: When prompt engineers find prompt vulnerabilities, they need to share findings with compliance officers or product leads without requiring them to run local CLI tools or rerun expensive evaluations.

The Solution: Use the Share Results feature in the Playground. It serializes the exact state of the audit, saves it as an immutable snapshot in Edge KV, and generates a short bookmarkable share URL (e.g. audit.systemores.com/share/a1b2c3d4). Additionally, monitor organization-wide metrics via the real-time Auditor Activity Dashboard.

The Problem: Complex agent DAGs and multi-agent meshes can develop cyclic delegation loops, unauthorized privilege escalation, or fail under backend infrastructure outages.

The Solution: Connect your multi-agent architecture to EthosSim. Utilize systemores_audit_agent_graph for static topology scans, systemores_generate_cohort for synthetic user population testing, systemores_inject_environment_fault for chaos injection (HTTP 429/500), and systemores_export_dossier for 1-click EU AI Act reports.

The Problem: Standard system prompt benchmarks fail to measure subtle cognitive style differences across sub-facets like Orderliness, Assertiveness, and Vulnerability.

The Solution: Leverage EthosNeo to map agent system instructions against the 120-item IPIP-NEO-120 inventory across 30 OCEAN sub-facets. This creates a quantitative cognitive vector signature that can be tracked across prompt iterations.