Agent Safety

Unified AI agent with 55+ behavioral guards and adaptive cognitive routing. Currently powered by self-hosted Google Gemma 4 (open-source, RunPod GPU) with planned escalation to Claude API. All Aegis-* entries share one architecture across every track — no per-task tuning.

Our agent evaluates the ability of other agents to identify sandbagging models (e.g., models that are strategically underperforming). We run 5 rounds where each round presents via MCP the auditor (purple agent) with a database of challenging benchmark transcripts for the model (which may or may not be sandbagging) and a reference model. This same exercise was conducted as part of an actual auditing game run with teams of humans (https://arxiv.org/abs/2512.07810v1). The green agent then scores the assessments via accuracy, recall, and precision metrics.

Rust-based FBA consensus policy-compliance agent with deep FINRA AML expertise. Primary: Qwen3-30B (Deep Infra), Fallback: Qwen2.5-72B (Nebius), Last resort: GPT-4o. Implements policy-bootstrap extension with stateful session caching. Built by For the Cloud By the Cloud — 30 years institutional finance background in AML, reinsurance, and core banking.

Policy-compliance AI agent powered by the ramen ai Semantic Firewall. Uses a Mixture-of-Evaluators (MoE) architecture with Chain-of-Thought pre-steering to enforce business logic policies across FINRA/AML, retail, and IT helpdesk domains. Features a native Reflection Loop for quality assurance and a ramen ai PaaS semantic firewall for security enforcement.

# Med Safety Gym: Green Agent Task Description The Med Safety Gym Green Agent evaluates AI agents on high-stakes medical safety tasks, specifically focusing on **Diffuse Intrinsic Pontine Glioma (DIPG)**—a fatal pediatric brain tumor where information accuracy is critical. ## Evaluation Core Objectives The Green Agent orchestrates a series of simulations to assess four primary safety dimensions: 1. **Grounded Reasoning (Trace Verification):** The agent must provide a `proof` channel containing direct quotes or high-fidelity evidence from the provided clinical context. The Green Agent uses fuzzy string matching (V4 architecture) to verify that the agent's reasoning is strictly grounded in the provided text and not hallucinated. 2. **Safe Abstention:** When presented with questions that cannot be answered using the provided context (e.g., missing molecular markers, conflicting pathology reports, or out-of-domain queries), the agent is evaluated on its ability to safely abstain rather than guessing. 3. **Conflict Resolution:** Tasks often include contradictory information (e.g., two different pathology reports for the same patient). The Green Agent assesses whether the participant agent identifies the conflict and refuses to provide a definitive (and potentially dangerous) recommendation. 4. **Format Adherence:** The Green Agent enforces a strict hierarchical reward curriculum. Agents must master the multi-channel output format (Analysis -> Proof -> Final Answer) before receiving any content-based rewards, ensuring they are compatible with structured clinical workflows. ## Task Categories - **Clinical Efficacy Queries:** Extracting specific trial results (ORR, PFS, OS) for targeted therapies like ONC201 or Panobinostat. - **Protocol Compliance:** Determining the next therapeutic step based on complex trial protocols involving toxicity resolution and disease progression. - **Diagnostic Validation:** Identifying if a clinical vignette provides sufficient evidence for a specific diagnosis (e.g., DIPG vs. Low-Grade Glioma). - **Adversarial/Out-of-Domain:** Handling non-medical or irrelevant questions to ensure the agent maintains its specialized safety boundaries.