AI-Powered Campaign Automation & Optimization
Implements production-grade agentic automation using LangGraph-style orchestration, event sourcing architecture, and distributed tracing. 6 specialized agents autonomously retrieve data, detect anomalies, perform root cause analysis, generate recommendations, and execute approved optimizations via platform APIs—with full audit trails, rollback capabilities, and human-in-the-loop safeguards.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Campaign Automation - Orchestration Engine
Campaign Automation - Analysis Results
Campaign Automation - Findings & Recommendations
Campaign Automation - Execution Summary
AI Agents
Specialized autonomous agents working in coordination
Workflow Orchestrator Agent
Complex automation workflows require stateful coordination, failure handling, and proper sequencing across multiple agents with varying execution times.
Core Logic
Manages workflow state machine using event sourcing patterns. Coordinates agent activation sequences, handles parallel execution where possible, manages dependencies between analysis and execution phases. Tracks causation chains across all events for complete auditability. Implements circuit breaker patterns for fault tolerance and maintains session correlation IDs for distributed tracing.
Data Retrieval Agent
Optimization requires fresh campaign data from multiple platforms, historical baselines, and industry benchmarks—all with different APIs and data freshness requirements.
Core Logic
Tools: `fetch_campaigns`, `vector_search`, `get_benchmarks`. Retrieves real-time metrics from Google Ads, Meta, LinkedIn, and TikTok APIs with automatic pagination and rate limit handling. Performs semantic search over knowledge base for historical context. Fetches 2025 industry benchmarks for comparison. Normalizes all data into unified schema with metadata enrichment and validation.
ML Analysis Agent
Detecting meaningful anomalies requires sophisticated statistical methods that distinguish signal from noise and account for seasonality and external factors.
Core Logic
Tools: `isolation_forest`, `statistical_test`, `trend_analysis`. Applies Isolation Forest algorithm for multivariate anomaly detection. Performs Z-score and T-test for statistical significance. Uses STL decomposition for seasonal adjustment. Calculates anomaly scores with confidence intervals. Classifies findings by type (Anomaly, Opportunity, Risk, Insight) and severity with supporting statistical evidence.
Root Cause Reasoning Agent
Identifying why anomalies occur requires connecting patterns across multiple data sources and reasoning through causal chains that humans might miss.
Core Logic
Tools: `rag_query`, `hypothesis_test`. Leverages RAG pipeline to search historical analyses for similar patterns. Generates hypotheses using chain-of-thought reasoning. Tests hypotheses against data using causal inference methods. Retrieves relevant documents with citation tracking. Produces root cause analysis with confidence scores, supporting evidence, and alternative explanations ranked by likelihood.
Optimization Recommendation Agent
Translating analysis findings into actionable recommendations requires understanding platform capabilities, budget constraints, and potential downstream effects.
Core Logic
Tools: `impact_forecast`, `budget_optimizer`. Generates prioritized recommendations (Critical/High/Medium/Low) based on findings. Forecasts expected impact with confidence intervals and statistical power analysis. Considers constraints (max budget change %, minimum ROAS thresholds). Produces implementation steps, risk assessments, and alternative approaches. Provides actionable optimization insights.
Automated Execution Agent
Implementing optimization recommendations across platforms is error-prone, time-consuming, and requires technical expertise that marketing teams often lack.
Core Logic
Tools: `platform_api`, `create_snapshot`. Executes approved actions via platform APIs: pause/resume campaigns, adjust budgets (increase/decrease/reallocate), modify bidding strategies, update targeting parameters, initiate A/B tests. Creates pre-execution snapshots for rollback capability. Tracks execution status, captures before/after state comparison, and logs complete audit trail. Supports dry-run mode for simulation without changes.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization