AI Yield Optimization Agent
## Solution An agentic AI system continuously analyzes real-time market conditions, demand signals, competitor positioning, and historical patterns to recommend and execute optimal floor price adjustments. The multi-agent architecture combines anomaly detection, price optimization, risk assessment, and autonomous execution with human approval gates for significant changes.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Input Configuration screen with anomaly detection for Mobile Sports segment and agent configuration options.
AI Agent Workspace showing chain-of-thought reasoning, tool executions, and inter-agent messaging with 68% confidence.
AI Recommendation showing root cause analysis with 94% confidence and floor price optimization to $3.90.
Optimization Results showing +$6,200/hour revenue impact, 74% fill rate, and $54.3M projected annual gain.
AI Agents
Specialized autonomous agents working in coordination
Workflow Orchestrator Agent
## Purpose Coordinates the yield optimization workflow, managing agent task assignment, progress tracking, and workflow state transitions across analysis, recommendation, and execution phases.
Core Logic
## Approach Initializes workflow sessions with input context, dispatches specialized tasks to data analyst, anomaly detector, price optimizer, and other agents, monitors individual agent progress and confidence scores, aggregates findings into unified recommendations, and manages transitions between workflow screens. Tracks real-time metrics including active agents, tool calls, token usage, and estimated session cost.
Data Analyst Agent
## Purpose Provides comprehensive analysis of current inventory segment performance, historical baselines, and data quality assessment to inform optimization decisions.
Core Logic
## Approach Retrieves current metrics including revenue rate, fill rate, eCPM, impressions, bid volume, and win rate. Compares against historical baselines with standard deviation calculations, validates data quality and completeness, and identifies metric anomalies or data gaps that could impact optimization accuracy. Provides segment-level breakdowns across device, geography, and content categories.
Anomaly Detector Agent
## Purpose Identifies abnormal patterns in revenue, demand, partner performance, or inventory behavior that require investigation or urgent intervention.
Core Logic
## Approach Applies statistical anomaly detection algorithms to real-time metric streams, categorizes detected anomalies by severity (critical, warning, moderate), generates root cause hypotheses with supporting evidence, and triggers appropriate alerts or escalations. Monitors for demand drops, demand surges, partner issues, and technical problems with early warning signal detection.
Price Optimizer Agent
## Purpose Determines optimal floor price adjustments that maximize revenue while maintaining acceptable fill rates and minimizing risk.
Core Logic
## Approach Analyzes price optimization curves across the floor price range, calculates predicted fill rates, revenue, and eCPM for each price point, identifies the optimal floor price with margin of error estimates, and generates recommendations with expected business impact. Considers current market conditions, seasonal factors, and competitive positioning in optimization calculations.
Risk Assessor Agent
## Purpose Evaluates potential risks associated with recommended floor price changes and develops mitigation strategies and rollback plans.
Core Logic
## Approach Assesses overall risk level based on change magnitude, market volatility, and historical accuracy. Identifies specific risk factors including fill rate degradation, partner churn, revenue volatility, and competitive response. Defines automatic rollback triggers and monitoring thresholds, establishes phased rollout plans with validation checkpoints to minimize implementation risk.
Execution Agent
## Purpose Implements approved floor price changes through phased deployment with continuous monitoring, A/B testing, and automatic rollback capabilities.
Core Logic
## Approach Executes multi-phase deployment starting with limited traffic exposure, monitors control vs test group performance with statistical significance calculations, validates success criteria at each phase before progression, and automatically triggers rollback if performance degrades beyond defined thresholds. Provides real-time execution progress with before/after metrics comparison.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
7 technologies
Architecture Diagram
System flow visualization