Multi-Agent FDA Document Review
6-agent AI system for FDA document review—60-70% time savings, multi-million USD ROI
The Problem
Regulatory Document Review Challenge
Pharmaceutical companies submit thousands of pages of documentation to the FDA for drug approval. Each document requires comprehensive quality review for:
- Grammar and spelling errors
- Formatting consistency
- Cross-reference accuracy
- Abbreviation definitions
- Summary-to-detail alignment
Manual review is:
- Labor-intensive (regulatory professionals spending hours on repetitive analysis)
- Inconsistent (different reviewers catch different issues)
- Expensive (specialized expertise required, $150-300/hour)
- Time-pressured (FDA submission deadlines are fixed)
The Cost
- Each document requires 4-8 hours of manual review
- A single submission may include dozens of documents
- Inconsistent review quality across different reviewers
The Solution
Multi-Agent Architecture
Rather than a single monolithic LLM prompt, the system uses 6 specialized agents, each focused on a specific analysis domain:
Why Multi-Agent?
| Approach | Pros | Cons |
|---|---|---|
| Single prompt | Simple | Token limits, inconsistent coverage, hard to maintain |
| Multi-agent | Specialized expertise, maintainable, parallel processing | Coordination complexity, deduplication needed |
The multi-agent approach delivers:
- Specialization: Each agent optimized for its domain
- Parallelization: 50+ concurrent API calls
- Maintainability: Update one agent without affecting others
- Extensibility: Add new analysis types as agents
Agent Specifications
| Agent | Purpose | Developed By |
|---|---|---|
| Grammar | Spelling, grammar, punctuation errors | Me |
| Format | Formatting consistency, printing issues | Me |
| Consistency | Document-wide pattern detection | Me |
| References | Citation and cross-reference validation | Me |
| Abbreviations | Abbreviation definition tracking | Me |
| Summary-to-Detail | Gap identification between sections | Team member |
Implementation
Cloud Architecture
Technology Stack
| Component | Technology | Rationale |
|---|---|---|
| Compute | Cloud Functions (2nd gen) | Event-driven, auto-scaling, serverless |
| AI Model | Vertex AI Gemini 1.5 Flash | Fast, cost-effective, large context window |
| Orchestration | LangChain | Agent abstraction, prompt management |
| Document Processing | PyMuPDF, python-docx | Robust PDF/DOCX extraction |
| Infrastructure | Terraform | Reproducible deployments |
Deduplication Algorithm
Multi-agent + multi-shot analysis generates duplicate findings. The deduplication engine uses:
- Similarity Scoring: 50% sentence content + 50% suggestion content
- Thresholds: Exact match ≥98%, Fuzzy match ≥85%
- Cross-batch comparison: Findings from different chunks/shots compared
Results: 15-40% duplicate removal across multi-shot analyses.
GxP Compliance
GxP (Good Practice regulations) governs quality and traceability in pharmaceutical manufacturing. The system addresses these requirements:
| Requirement | Implementation |
|---|---|
| Audit trail | Cloud Logging captures all processing decisions |
| Reproducibility | Configuration-driven, versioned prompts |
| Traceability | Document → Chunk → Finding → Report lineage |
| Access control | GCP IAM with least-privilege roles |
| Data integrity | Immutable GCS storage for inputs/outputs |
My Role
| Responsibility | Activities |
|---|---|
| Solution Architect | Designed multi-agent architecture, deduplication strategy, cloud infrastructure |
| Agent Developer | Built 5 of 6 specialized analysis agents |
| Project Manager | Timeline, deliverables, client communication |
| Client Liaison | Requirements, demos, business case validation |
Team Structure
- Me: Architecture, 5 agents, PM, client liaison
- Team member: Summary-to-Detail agent
- Client stakeholders: Regulatory leadership, QA team, IT
Results
Performance Metrics
| Metric | Value |
|---|---|
| Processing speed | ~18 seconds/page |
| 5-page document | 1m 23s |
| 33-page document | 10m 12s |
| 100+ page document | Linear scaling (~18s/page) |
| Concurrent API calls | 50+ |
| Deduplication rate | 15-40% |
Business Impact
| Metric | Impact |
|---|---|
| Manual review reduction | 60-70% estimated time savings |
| ROI projection | Multi-million USD savings (client-validated) |
| Ongoing investment | 1 FTE continuing development |
| Strategic partnership | Google Cloud co-sponsorship negotiated |
vs. Manual Review
| Manual Review | AI-Assisted Review |
|---|---|
| 4-8 hours per document | 30 minutes processing + 1-2 hours review |
| Reviewer fatigue affects quality | Consistent quality on every document |
| Limited by headcount | Scales automatically |
| Variable coverage | Comprehensive 6-dimension analysis |
Lessons Learned
-
Multi-agent coordination requires deduplication. Different agents analyzing the same content will find overlapping issues. Semantic deduplication is essential.
-
Rate limiting strategy needs ceiling. Simple exponential backoff can grow unbounded. Implement max delay (61s) with reset.
-
Document format affects chunking strategy. PDF and DOCX require different approaches—page-based vs. logical element-based.
-
Externalize configuration. Prompt updates without redeployment dramatically speeds iteration.
-
Persona ordering prevents confusion. When multiple agents report similar findings, consistent ordering helps users process results.
Impact
AI-assisted document review changed the economics of FDA submissions for this client:
- 60-70% reduction in manual review time on validation reports—regulatory professionals focus on judgment calls, not grammar checking
- Consistent coverage across every document—no more variability between reviewers or reviewer fatigue on page 80
- Scalable capacity during submission crunch periods—the system handles volume spikes without headcount
The client validated a multi-million USD savings projection and committed 1 FTE to continue development. Google Cloud co-sponsorship negotiations are underway for expanded deployment.
Want to discuss AI document review?
Building a similar system for document review, compliance, or quality assurance? I’ve shipped this pattern across pharma, biotech, and enterprise contexts. Get in touch.