AI-Powered Multi-Agent Campaign Orchestration & Optimization Platform

Core Logic

The Orchestrator Agent serves as the command center for the entire campaign intelligence workflow, managing initialization and coordination across all other agents. Operating in Phase 1 (Initialization, 2-second duration), it establishes execution context, generates TraceIds for distributed tracing, initializes agent states, and creates the execution plan. The agent implements the circuit breaker pattern to prevent cascade failures—if any agent fails 5 consecutive times, the circuit opens and routes work to fallback paths. It manages resource allocation (CPU, memory, GPU) across agents, tracks progress through the 7-phase execution (total ~29 seconds with parallelization), and emits SystemEvents for real-time monitoring. The orchestrator supports simulation speed control (1x, 2x) for demonstration purposes and maintains comprehensive audit logs of all coordination decisions. Core capabilities include multi-phase workflow coordination, circuit breaker implementation, resource allocation management, distributed trace generation, event emission and monitoring, and failure recovery orchestration. Architectural patterns implemented include Circuit Breaker, Event-Driven, and Observer patterns. Monitoring features include agent status tracking, progress reporting, resource utilization tracking, and error rate monitoring.

Core Logic

The Data Ingestion Agent handles Phase 2 (Data Ingestion, 4-second duration) of the workflow, connecting to and ingesting data from five primary sources: MEMBER_DB (core demographics and account data), CLAIMS (transaction and claim history), CRM (interaction and engagement data), BEHAVIORAL (website, email, and app activity), and DEMOGRAPHIC (external enrichment data). The agent validates connection latency (10-150ms acceptable), performs schema validation with status indicators (VALID/WARNING/ERROR), executes ETL jobs with row count tracking, and implements data lineage tracking for audit purposes. It detects and classifies sensitive data fields (PHI, PII, SENSITIVE) to ensure appropriate handling downstream. Data quality scores (85-99%) are calculated for each source, and the agent produces validated, standardized datasets for the Feature Engineering Agent. Technical metrics include connection health monitoring, throughput tracking, and comprehensive error logging. Core capabilities include multi-source data connection, schema validation, ETL job execution, data lineage tracking, PII/PHI detection, and quality scoring. Quality checks performed include connection latency validation, schema validation, row count verification, data freshness assessment, and completeness scoring. Output types include validated datasets, quality reports, and lineage graphs.

Core Logic

The Feature Engineering Agent executes Phase 3 (Feature Engineering, 5-second duration), transforming raw data into high-quality features for downstream ML models. It creates features across four types: NUMERIC (continuous values like age, income, engagement scores), CATEGORICAL (discrete categories like plan type, region, segment), EMBEDDING (vector representations for text and behavioral sequences), and TEMPORAL (time-based features like recency, seasonality, trends). The agent implements RFM analysis with O(n log n) complexity for customer value scoring, calculates feature importance scores for model explainability, tracks null rates to ensure data quality, and generates feature metadata for lineage tracking. It produces feature stores with standardized schemas, enabling consistent model training and inference. Healthcare-specific features include HEDIS measure flags, care gap indicators, SDoH factors, and engagement scores. Core capabilities include RFM analysis and scoring, categorical encoding, embedding generation, temporal feature extraction, feature importance calculation, and null rate tracking. Algorithms implemented include RFM Analysis with O(n log n) complexity for customer value scoring based on Recency, Frequency, Monetary metrics. Output types include feature stores, feature metadata, and importance rankings.

Core Logic

The Segmentation Agent executes Phase 4 (Segmentation, 6-second duration), creating intelligent customer segments using ML clustering and propensity modeling. It implements collaborative filtering with O(m × n) complexity for recommendation-based segmentation, calculates propensity scores with confidence intervals for each segment, maps customers to journey stages (AWARENESS, CONSIDERATION, DECISION, RETENTION), and produces SHAP-like feature contributions for segment explainability. The agent creates segment profiles including characteristics, selection criteria, demographic distributions, and predicted responsiveness to different channels and messages. For healthcare contexts, it incorporates SDoH factors (economic stability, education, healthcare access, neighborhood, social context) and health engagement scores. Output segments include size, propensity scores, channel preferences, and recommended journey paths. Core capabilities include ML-based clustering, propensity score calculation, confidence interval estimation, journey stage mapping, SHAP explainability, and SDoH integration. Algorithms implemented include Collaborative Filtering with O(m × n) complexity for recommendation-based segmentation using cosine similarity. Output types include customer segments, propensity scores, journey mappings, and feature contributions.

Core Logic

The Journey Optimizer Agent executes in Phase 5 (Journey Optimization, 7 seconds, parallel with Content Generator), designing optimal customer journeys using graph-based optimization. It implements Dijkstra's algorithm with O((V+E) log V) complexity to find optimal paths through journey touchpoints, considers conditional rules (PROCEED, SKIP, BRANCH, WAIT, ESCALATE) for dynamic journey adaptation, and optimizes across multiple channels (EMAIL, SMS, PRINT, VIDEO, WEB, PHONE) based on segment preferences. The agent creates touchpoint sequences with timing, channel, content type, and expected engagement for each step. It incorporates A/B testing intelligence with Bayesian probability calculations for variant selection and supports causal inference modeling for treatment effect estimation. Journeys are designed to maximize conversion probability while minimizing cost per acquisition and maintaining regulatory compliance. Core capabilities include graph-based journey optimization, conditional rule processing, multi-channel coordination, touchpoint sequencing, A/B test integration, and conversion probability modeling. Algorithms implemented include Dijkstra's Algorithm with O((V+E) log V) complexity for optimal path finding through journey touchpoints. Output types include journey maps, touchpoint sequences, timing schedules, and channel allocations.

Core Logic

The Content Generator Agent executes in Phase 5 (Content Generation, 7 seconds, parallel with Journey Optimizer), creating personalized content using generative AI capabilities. It generates content across six types: EMAIL (subject lines, body copy, CTAs), SMS (concise messages with compliance), PRINT (letters, postcards, brochures), VIDEO (scripts with personalization points), WEB (landing pages, banners), and SCRIPT (call center talking points). Each content piece includes quality metrics (relevance score, coherence score, personalization depth), compliance flags for regulatory requirements, and emotional tone analysis (professional, empathetic, urgent, celebratory). The agent respects segment-specific preferences, incorporates SDoH-informed messaging for healthcare contexts, and generates multiple variants for A/B testing. Content is validated against regulatory requirements before output, with automatic flagging of potential compliance issues. Core capabilities include GenAI content generation, multi-channel content adaptation, personalization at scale, quality scoring, compliance validation, and emotional tone optimization. Personalization factors include segment characteristics, journey stage, channel preferences, SDoH factors, and historical engagement.

Core Logic

The Budget Allocator Agent executes Phase 6 (Budget Optimization, 3-second duration), optimizing budget allocation using advanced mathematical methods. It implements linear programming using the Simplex method with O(n³) complexity for optimal allocation across channels and segments, runs Monte Carlo simulation (10,000 iterations) for confidence interval estimation on projected returns, and applies exponential smoothing with O(n) complexity for time-series forecasting of channel performance. The agent calculates predicted conversions, ROI, and cost per acquisition for each channel allocation, considers constraints (minimum/maximum spends, channel capacity, regulatory limits), and produces allocation recommendations with confidence intervals. Attribution modeling using Markov chains with O(n²) complexity determines channel contribution to conversions. Output includes detailed budget breakdowns, projected performance metrics, and optimization recommendations with reasoning. Core capabilities include linear programming optimization, Monte Carlo simulation, time-series forecasting, attribution modeling, constraint optimization, and ROI projection. Algorithms implemented include Linear Programming Simplex with O(n³) complexity for optimal budget allocation, Monte Carlo Simulation with 10,000 iterations for confidence interval estimation, Markov Chain Attribution with O(n²) complexity for multi-touch attribution, and Exponential Smoothing with O(n) complexity for time-series performance forecasting. Attribution models supported include LAST_TOUCH, FIRST_TOUCH, LINEAR, TIME_DECAY, MARKOV, and SHAPLEY. Output types include budget allocations, ROI projections, attribution reports, and optimization recommendations.

Core Logic

The Compliance Validator Agent executes Phase 7 (Compliance Validation, 2-second duration), validating all campaign components against five major regulatory frameworks: HIPAA (PHI handling, minimum necessary, business associate requirements), CAN-SPAM (email compliance, unsubscribe requirements, sender identification), TCPA (SMS/phone consent, time restrictions, DNC list compliance), GDPR (EU data subject rights, consent, data portability), and CCPA (California privacy rights, disclosure requirements, opt-out rights). The agent performs automated checks on content, targeting criteria, channel configurations, and data handling practices. It produces comprehensive compliance reports with status indicators, specific findings, remediation recommendations, and risk scores. All validation decisions are logged to immutable audit trails with cryptographic hashing. The agent can halt campaign deployment if critical compliance failures are detected, and generates audit-ready documentation for regulatory review. Healthcare-specific validations include CMS Star Ratings compliance, Medicare Marketing Guidelines adherence, PHI handling verification, and consent management. Core capabilities include multi-framework compliance validation, content compliance checking, consent verification, audit trail generation, risk scoring, and remediation guidance. Enforcement actions include warning generation, deployment blocking, escalation triggers, and remediation tracking. Output types include compliance reports, risk assessments, remediation plans, and audit documentation.