Multi-Agent Campaign Optimization Platform
Deploys 7 specialized AI agents that continuously analyze campaign performance and make autonomous optimization decisions within configurable guardrails. The system features human-in-the-loop approval workflows for high-impact changes while auto-executing low-risk optimizations.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Agent Configuration - Setup for Budget Optimizer, Bid Manager, Audience Analyst, and Creative Optimizer
Mission Control Dashboard - Real-time agent orchestration and campaign performance monitoring
Execution Results - Performance summary with revenue impact and agent ROI attribution
Agent Collaboration Network - Inter-agent communication and decision workflow visualization
AI Agents
Specialized autonomous agents working in coordination
Supervisor Agent - Orchestrator
Complex optimization requires coordinating multiple specialized agents, decomposing high-level goals into executable tasks, and resolving conflicts between competing recommendations.
Core Logic
Analyzes campaign briefs and decomposes them into specific optimization tasks. Delegates tasks to specialized agents based on task type. Monitors progress, resolves conflicts between agent recommendations, and compiles final optimization reports with ROI attribution per agent.
Budget Optimizer - Financial Strategist
Budget allocation across segments needs continuous rebalancing based on marginal ROI. Static allocations leave high-performing segments underfunded while wasting budget on underperformers.
Core Logic
Analyzes segment-level ROAS and marginal returns using LP optimization (Simplex algorithm). Calculates optimal budget reallocation respecting guardrails (max 20% change per segment). Generates detailed reallocation plans with expected conversion and revenue impact projections.
Bid Manager - Auction Strategist
Real-time bidding requires understanding auction dynamics, competitor behavior, and optimal bid levels. Static bids result in either overpaying or losing valuable impressions.
Core Logic
Uses contextual bandit (reinforcement learning) models to simulate bid changes across 1000+ scenarios. Analyzes bid landscape and win rates per placement. Generates bid adjustment recommendations with projected CPA reduction and conversion uplift.
Audience Analyst - Growth Specialist
Finding new high-value audience segments requires analyzing converter characteristics and identifying lookalike opportunities. Manual audience discovery is slow and misses expansion opportunities.
Core Logic
Analyzes top converter characteristics across demographic, behavioral, and contextual features. Generates lookalike audiences with similarity scores (minimum 87% threshold). Designs A/B test plans with statistical power calculations and success criteria.
Creative Optimizer - Creative Strategist
Creative performance degrades over time due to fatigue. Identifying top performers and fatigued creatives requires continuous monitoring and rapid rotation adjustments.
Core Logic
Uses Thompson Sampling multi-armed bandit algorithms to rank creative variations with confidence intervals. Detects creative fatigue patterns (18% CTR decline threshold). Generates optimal rotation weights balancing exploitation of top performers with exploration of new variants.
Anomaly Detector - Security Guardian
Ad fraud and invalid traffic waste budget and damage advertiser trust. Detecting sophisticated fraud requires real-time analysis of traffic patterns that humans cannot monitor continuously.
Core Logic
Runs Isolation Forest anomaly detection on traffic patterns with configurable sensitivity thresholds. Classifies anomalies by type (fraud, technical, competitive, seasonal). Auto-blocks confirmed fraud patterns (94%+ confidence) while flagging uncertain cases for human review.
Predictive Intelligence - Strategic Forecaster
Proactive optimization requires anticipating performance changes, budget depletion, creative fatigue, and market shifts before they impact results. Reactive optimization wastes budget.
Core Logic
Generates ROAS trajectory forecasts using ARIMA and Gradient Boosting ensembles. Predicts creative fatigue 48 hours in advance using neural network models. Runs Monte Carlo budget simulations for pacing optimization. Identifies high-intent conversion windows using temporal pattern recognition.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
8 technologies
Architecture Diagram
System flow visualization