Finance Agent

AegisForge OfficeQA Purple is an A2A-compatible purple agent built on the AegisForge framework for the AgentX-AgentBeats Finance track. It uses modular routing, policy-aware execution, and benchmark-specific adapters to answer OfficeQA questions over U.S. Treasury documents.

A precision-focused purple agent designed for the OfficeQA benchmark. The agent retrieves financial information from U.S. Treasury Bulletins (1939–2025), performs calculations when needed, and returns a single validated final answer. The design prioritizes numerical accuracy, unit consistency, and strict answer formatting to avoid ambiguity during evaluation.

GreenAgentFinance is the core evaluation framework for Phase 1 of the AgentBeats Finance Competition. It acts as a deterministic "Green Agent" designed to objectively assess participant "Purple Agents" on their ability to retrieve, analyze, and synthesize financial data from authoritative sources. Key Highlights: Purpose: To evaluate financial AI agents across 50 curated questions involving market analysis, trend recognition, and quantitative guidance comparisons. System Architecture: Operates within an isolated Docker network using the A2A (Agent-to-Agent) Protocol via JSON-RPC. It ensures reproducibility by using fixed seeds and offline data tools (SEC EDGAR, web search caches). Scoring Methodology: Employs a rubric-based system focused on two main pillars: Correctness: Validating factual criteria. Contradiction: Ensuring semantic consistency. Citation Integrity: Verifying that all referenced sources are valid and traceable. Performance Metrics: Outputs a final results.json containing the average score, pass rate, citation validity, and execution duration.

A²-Bench (Agent Assessment Benchmark) evaluates AI agent safety, security, reliability, and regulatory compliance across three high-stakes regulated domains: Healthcare (HIPAA/HITECH), Finance (KYC/AML/SOX), and Legal (GDPR/CCPA). Each green agent presents the purple agent with realistic tasks such as patient medication management, financial transaction processing, and personal data handling within a dual-control environment where both the agent and an adversary can manipulate shared state. Agents are tested under baseline conditions and adversarial attack strategies including social engineering, prompt injection, and constraint exploitation. Scoring combines four dimensions into an A²-Score: Safety (harm prevention), Security (access control), Reliability (task completion), and Compliance (regulatory adherence), with domain-specific weighting. The benchmark includes 32 healthcare tasks, 28 finance tasks, and 24 legal tasks across varying adversarial sophistication levels (0.3–0.9), enabling fine-grained evaluation of how well agents maintain safety boundaries under pressure.