Scenario 1: Multi-Agent Legal Due Diligence Orchestration System

Core Logic

The Orchestrator Agent serves as the central nervous system of the due diligence workflow, coordinating all other agents using GPT-4 Turbo with a low temperature (0.3) for consistent decision-making. It manages task routing to appropriate specialist agents based on current workflow phase and document characteristics, monitors progress across all six workflow phases, ensures quality control by validating agent outputs before handoff, coordinates inter-agent communication through structured message passing (task assignment, status update, data transfer, query, response, handoff, broadcast), maintains workflow state and metrics, and triggers Human-in-the-Loop approval requests when findings exceed confidence thresholds or risk levels. The agent has access to task_router, quality_checker, and progress_monitor tools and maintains a 98% accuracy rate in task delegation.

Core Logic

The Document Analyst Agent uses Claude 3 Opus with very low temperature (0.1) for precise, deterministic extraction. It processes documents through OCR using Azure Document Intelligence, extracts contract clauses using advanced NLP with 95% accuracy, performs named entity recognition to identify parties, dates, amounts, and jurisdictions, analyzes document structure to understand section hierarchy and cross-references, and categorizes documents by type and risk profile. The agent operates specialized tools: ocr_processor (3.5s avg execution, 98% success rate), clause_extractor (2.2s avg, 95% success), entity_recognizer (1.5s avg, 93% success), and structure_analyzer. It tracks metrics including tasks completed, tokens used, tool calls, average response time, and maintains a 95% accuracy rate.

Core Logic

The Risk Assessor Agent employs GPT-4 Turbo with low temperature (0.2) for analytical precision. It evaluates contract risks using logistic regression scoring models, calculates financial exposure using Monte Carlo simulation (1000 iterations) with 95% confidence intervals, performs trend analysis to identify risk patterns across document portfolios, detects anomalies that may indicate hidden risks, and categorizes risks by type (Liability, Financial, Compliance, Operational, Reputational, Legal, Data Privacy) and severity (Critical, High, Medium, Low, Info). The agent generates detailed findings with evidence, impact assessment (financial min/max estimates, legal implications, operational considerations, timeline urgency), and actionable recommendations. It triggers HITL approval requests for critical findings exceeding defined thresholds, ensuring human oversight for high-stakes decisions. Tools include risk_scorer (800ms avg, 97% success), financial_modeler, monte_carlo_simulator (2.5s avg, 98% success), and anomaly_detector.

Core Logic

The Compliance Checker Agent uses Claude 3 Opus with very low temperature (0.1) for regulatory precision. It validates compliance against multiple frameworks: GDPR Article 28 requirements for data processing agreements, Spanish Civil Code provisions, Spanish Labor Law requirements, SOX controls, and HIPAA requirements where applicable. The agent identifies missing clauses (e.g., GDPR Art. 28(3)(a) documented instructions, Art. 28(3)(c) security measures, Art. 28(3)(h) data deletion), generates compliance findings with specific article references and remediation recommendations, calculates compliance rates and penalty exposure, and produces gap analysis reports showing current state vs required state. Tools include gdpr_validator (1.8s avg, 99% success rate), compliance_checker, regulation_lookup, and gap_analyzer. The agent maintains 97% accuracy in compliance determination.

Core Logic

The Legal Researcher Agent uses GPT-4 Turbo with moderate temperature (0.4) for creative research strategies. It searches legal precedent databases using semantic vector search (Pinecone) to find relevant cases and rulings, analyzes Spanish Supreme Court decisions, AEPD (Spanish Data Protection Authority) resolutions, and EU Court cases, provides legal interpretation for complex clause language, finds citations to support findings and recommendations, and cross-references multiple legal sources for comprehensive context. Key capabilities include natural language query processing across jurisdictions, relevance ranking of precedents by applicability, and citation extraction with proper legal formatting. Tools include case_database, precedent_search, legal_interpreter, citation_finder, and vector_search (450ms avg, 99% success). The agent achieves 92% accuracy in precedent relevance matching.

Core Logic

The Report Writer Agent uses Claude 3 Opus with moderate temperature (0.5) for natural language generation. It synthesizes findings from all agents into comprehensive executive summaries including overall risk score, risk level classification (Low/Medium/High/Critical), key metrics dashboard (documents analyzed, pages processed, clauses extracted, findings count, critical issues, compliance rate, processing time, estimated savings), top findings prioritized by severity, risk distribution visualization, compliance status across all frameworks, prioritized recommendations with effort/impact ratings, financial exposure breakdown by category, and timeline of critical deadlines. The agent generates multi-format outputs: Executive Summary for C-level, Detailed Report for legal teams, Risk Matrix for risk management, and Action Items for implementation teams. Tools include report_generator (4s avg, 96% success), summary_creator, chart_builder, and recommendation_engine. Achieves 96% accuracy in report quality.

Core Logic

The EU AI Act Auditor Agent uses Claude 3 Opus with very low temperature (0.1) for regulatory precision. It classifies AI systems according to EU AI Act risk categories (Unacceptable, High, Limited, Minimal), maps applicable articles based on system use case (Legal Interpretation, Contract Analysis, Compliance Assessment, Risk Prediction, Document Processing), validates transparency obligations (AI disclosure, data source transparency, limitation disclosure, human oversight), verifies human oversight levels (Human-in-the-Loop, Human-on-the-Loop, Human-in-Command), generates technical documentation requirements, audits compliance requirements against specific articles (Art. 9 Risk Management, Art. 10 Data Governance, Art. 12 Record-keeping, Art. 13 Transparency, Art. 14 Human Oversight, Art. 15 Accuracy/Robustness), identifies compliance gaps with severity ratings (critical, major, minor), and provides remediation recommendations with effort estimates. Tools include ai_act_classifier (2s avg, 98% success), transparency_checker (1.5s avg, 97% success), oversight_validator, and technical_doc_generator. Maintains 99% accuracy in EU AI Act compliance assessment.

Core Logic

The Predictive Analyst Agent uses GPT-4 Turbo with moderate temperature (0.3) for analytical forecasting. It performs ML-based risk prediction using historical contract data with confidence intervals, generates risk predictions across multiple time horizons (3, 6, 12, 24 months) by category (Liability, Compliance, Financial, Operational), identifies key risk drivers and their sensitivity weights, models scenario analysis (best case, expected, worst case) with probability weightings and financial impact, forecasts trend analysis for risk factor evolution (increasing, stable, decreasing), calculates aggregated risk scores with confidence intervals, and generates mitigation options ranked by effectiveness, cost, implementation time, and priority. The agent provides actionable insights including trigger events that could escalate risks, key assumptions underlying predictions, and strategy recommendations for risk reduction. Tools include risk_predictor (3.5s avg, 92% success), scenario_modeler (4.5s avg, 95% success), trend_analyzer, and monte_carlo_advanced. Achieves 91% accuracy in predictive modeling.

Core Logic

The Benchmark Analyst Agent uses GPT-4 Turbo with low temperature (0.2) for precise comparative analysis. It compares contracts against an industry benchmark database containing 50,000+ contract templates, evaluates key metrics (Liability Cap, Payment Terms, Termination Notice, Warranty Period, IP Assignment, Data Protection) against industry averages and best-in-class examples, calculates percentile rankings for each document and metric, identifies trend indicators (better than, average, worse than industry), assesses metric significance (high, medium, low) for prioritization, generates specific recommendations with current state, target state, potential savings, and implementation effort, and calculates aggregate potential savings across the portfolio. Benchmarking results include comparison group identification, sample size disclosure for statistical validity, strong areas and improvement areas summary, and detailed metric-by-metric analysis. Tools include benchmark_analyzer (3s avg, 94% success), clause_comparator, industry_database, and savings_calculator. Maintains 93% accuracy in benchmark comparisons.