Overview
Multi-agent eDiscovery systems transform the legal discovery process from expensive, time-consuming manual review to intelligent, automated analysis. Agent teams process millions of documents, identify relevant materials, detect privileged content, and prepare productions—dramatically reducing costs while improving accuracy.
Architecture
Data Sources → Collection Agent → Raw Data
↓
Processing Agent → Processed Documents
↓
Relevance Scoring Agent → Relevance Rankings
↓
Review Agent → Reviewed Documents
↓
Privilege Detection Agent → Privilege Flags
↓
Timeline Agent → Case Timeline
↓
Key Document Agent → Hot Documents
↓
Production Agent → Final Production
Agent Roles
Collection Agent
- Identifies relevant data sources
- Collects data preserving metadata
- Handles various data formats
- Ensures chain of custody
Processing Agent
- Deduplicates documents
- Extracts text and metadata
- Handles OCR for images
- Processes email threads
Relevance Scoring Agent
- Scores documents for relevance to issues
- Uses predictive coding/TAR techniques
- Learns from reviewer decisions
- Prioritizes review queue
Review Agent
- Assists human reviewers
- Suggests coding decisions
- Identifies similar documents
- Accelerates review process
Privilege Detection Agent
- Identifies potentially privileged documents
- Flags attorney-client communications
- Detects work product materials
- Prevents inadvertent production
Timeline Agent
- Constructs case timeline from documents
- Identifies key dates and events
- Connects documents chronologically
- Visualizes temporal patterns
Key Document Agent
- Identifies "hot" documents
- Finds smoking guns and key evidence
- Ranks documents by importance
- Alerts attorneys to critical findings
Production Agent
- Prepares documents for production
- Applies redactions
- Formats according to specifications
- Generates production logs
Technology-Assisted Review (TAR)
Traditional Review:
- Humans review every document
- $1-2 per document
- Weeks or months for large cases
TAR-Enhanced Review:
- AI prioritizes and suggests
- Humans review AI decisions
- 80-90% cost reduction
- Days instead of months
Real-World Results
Large Litigation Cases:
- Review of 10 million documents in weeks
- 90% cost reduction vs. manual review
- Higher accuracy than human-only review
- Defensible methodology
Accuracy Metrics:
- Recall rates of 80-95%
- Precision comparable to expert reviewers
- Consistent application of review criteria
Key Patterns
- Human-in-the-Loop: Attorneys validate AI decisions
- Reflection Pattern: Learn from reviewer feedback
- Guardrails Pattern: Prevent privilege breaches
- Audit Trail Pattern: Document all decisions for defensibility
Common Failure Modes
- Privilege Leaks: Privileged documents inadvertently produced
- Relevance Drift: AI model drifts from case issues
- Over-Reliance: Attorneys don't sufficiently validate AI
- Format Failures: Documents not properly processed
Regulatory Considerations
- Courts increasingly accept TAR methodologies
- Proportionality doctrine supports AI use
- Cooperation with opposing counsel on protocols
- Transparency about AI use in discovery