Multi-Agent Editorial Screening
A multi-agent editorial system that automates initial editorial review processes. Agents collaborate to review content for grammar, clarity, compliance, and scientific consistency, with direct PDF markup capabilities.
Key Impact: Projected 40%+ reduction in first-pass editorial effort
Overview
Architected a multi-agent orchestration system designed to streamline editorial review for pharmaceutical promotional materials. The system performs grammar, clarity, compliance, and scientific-consistency reviews using specialized AI agents that work collaboratively. A custom-built PDF MCP (Model Context Protocol) enables agents to markup documents directly, while recursive QA loops ensure quality governance before content reaches human reviewers.
Challenges
- ●Coordinating multiple specialized agents across different review domains (grammar, clarity, compliance, scientific accuracy)
- ●Building automated PDF annotation capabilities for seamless editorial feedback
- ●Designing recursive self-check loops that catch issues before human review
- ●Creating compliance-aware content output aligned with MLR expectations
Results
- ✓Projected 40%+ reduction in first-pass editorial processing time
- ✓Automated PDF annotation output for streamlined editorial feedback
- ✓Recursive QA governance supporting compliance-ready content
- ✓Reusable agent architecture built on Claude Agent SDK
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