Multi-Agent Based Automation of Mass Tort Litigation Document Triage

Manual linear review of medical records, claimant forms, and exposure histories in mass torts is a primary cost driver. A custom multi-agent workflow automates ingestion, classification, and routing, applying issue-specific classifiers and confidence scoring to route only ambiguous documents for human review. This shifts the bulk of the work from expensive contract attorneys to orchestrated AI agents, directly lowering the cost per document reviewed.

Waiting for a manual document inventory delays complaint drafting, settlement modeling, and expert selection. An autonomous triage workflow provides a near-real-time, categorized inventory of all plaintiffs and key evidence within days of data ingestion. Legal teams gain immediate visibility into case strengths, weaknesses, and scale, enabling faster, more informed strategic decisions that compress the overall litigation timeline.

Manual review under time pressure leads to inconsistent coding, missed linkages between plaintiffs and master complaints, and reviewer drift. The workflow uses specialized agents for standardization (e.g., normalizing medical codes), entity resolution (linking John Doe in Form A to J. Doe in Medical Record B), and rule-based validation. This creates a consistent, auditable classification layer that improves the quality of the dataset for downstream analysis and expert testimony.

In high-stakes litigation, the defensibility of the review process is paramount. The custom architecture logs every agent decision, confidence score, and human override, creating a transparent chain of custody for each document. This built-in governance layer provides ready-made responses to challenges on methodology, satisfying Federal Rule of Evidence 502 and protecting the work product from spoliation claims.

Manual processes break down at the scale of modern mass torts. The orchestrated workflow, built on frameworks like LangGraph, is designed for elastic scale. It can process millions of documents by dynamically coordinating ingestion agents, classification queues, and review queues without proportional increases in human labor. This turns document review from a limiting factor into a scalable, predictable operational function.

The value is realized through integration, not replacement. The workflow is architected as an orchestration layer that connects to existing e-discovery platforms (Relativity, Everlaw), cloud storage, and review management systems via APIs. It acts as a high-speed, intelligent pre-processor, feeding perfectly batched and categorized documents into the familiar review UI, minimizing change management and maximizing attorney adoption.

The first agent in the chain ingests raw documents (scanned PDFs, EHR exports, faxes) via secure connectors. It extracts text via OCR/LLM, normalizes patient identifiers using fuzzy matching against a master plaintiff list, and tags documents by type (e.g., diagnostic_report, prescription, exposure_history). This step converts fragmented inputs into a structured, query-ready data layer, eliminating the manual data entry and misidentification that plagues mass tort intake.

This agent processes normalized medical records to construct a timeline per plaintiff. It extracts dates, procedures, diagnoses, and prescriptions using a clinical NER model, then links documents to specific allegations in the master complaint (e.g., exposure_event_2018, diagnosis_date). The output is a unified patient narrative that automatically surfaces gaps in medical history for follow-up, a task that typically requires weeks of paralegal and nurse review.

The core orchestration layer (built with LangGraph or Temporal) applies rule-based and ML classifiers to scored documents. It routes high-confidence responsive records to the appropriate review team (medical, damages, causation), sends potentially_privileged items to a legal hold queue, and flags low_value or duplicate documents for secondary QC. This dynamic routing replaces static, linear batching, ensuring specialized reviewers only see documents pertinent to their expertise.

For low-confidence classifications, ambiguous documents, or potential privilege hits, the workflow pauses and creates a task in a review platform like Relativity or Everlaw. It presents the agent's reasoning, source excerpts, and suggested action to a human reviewer. Upon decision, the feedback loops back to train the classifiers. This controlled escalation preserves defensibility and attorney oversight while keeping human effort focused on edge cases, not volume.

Every agent action—document ingestion, classification score, routing decision, and human override—is logged to an immutable audit trail with full chain-of-custody. This agent automatically generates production-ready reports on review progress, document populations, and cost metrics. The defensible log is critical for meeting FRCP and court-ordered transparency requirements, turning automation from a black box into a court-admissible process.

Implementation connects to source systems (Secure FTP, cloud storage, EHR APIs) via managed connectors. The agent graph is deployed as containerized services on Kubernetes, with vector databases (Pinecone, Weaviate) for semantic search and orchestration state. Integration points with legacy e-discovery platforms (Relativity, Disco) use their APIs for seamless document transfer and queue management, allowing the custom workflow to augment, not replace, existing legal tech investments.

For sensitive PHI and privileged data, deploy orchestration agents and vector databases within a client's VPC or data center. This model keeps all PII/PHI on-prem, integrates directly with internal document management systems (iManage, NetDocuments), and satisfies strict data sovereignty requirements for healthcare and legal data. Use containerized agents (Docker, Kubernetes) for scaling review pods during ingestion peaks.

Structure the workflow as a state machine using LangGraph to manage specialized agents for classification, entity linking, and summarization. A Supervisor Agent routes documents between a Medical Record Parser, an Exposure History Linker, and a Claimant Form Validator. This design allows for precise logging of each decision path, creating an immutable audit trail for challenge and QC.

Embed the triage workflow as a pre-processing layer feeding into existing review platforms. Ingest native files and metadata via Relativity Script or Everlaw API, run classification and clustering, then push results back as custom fields and batch sets. This allows legal teams to use familiar interfaces for QC and production while automating the initial sort of millions of documents.

Implement confidence-based routing to paralegal or attorney review queues. Documents with low confidence scores, conflicting classifications, or potential privilege flags are automatically placed in an Exception Queue within the review platform. This controlled escalation preserves human oversight for edge cases while automating the clear-cut majority, ensuring defensibility and managing risk.

Choose the ingestion pattern based on case timeline. Batch Processing is optimal for initial data dumps: process millions of documents overnight, generate master reports, and populate review platforms by morning. Real-Time Streaming handles continuous production from new custodians, applying triage logic as documents land, enabling near-instant assignment to appropriate case teams.

Instrument the workflow with Prometheus/Grafana dashboards tracking agent accuracy, queue depths, and processing throughput. Log all LLM calls, classification decisions, and routing events to a centralized audit log (e.g., Elasticsearch) for immediate troubleshooting and long-term defensibility. This operational visibility is critical for managing SLA commitments during large-scale tort proceedings.