AI Workflow for Multi-Agent Based Validation of Data Traceability from Source to Submission

Manual verification of data lineage across EDC, SDTM, ADaM, and submission documents is a high-effort, error-prone process that consumes weeks of biostatistician and programmer time before each filing. A custom agentic workflow automates this by deploying specialized validators that map data points across systems, flag breaks in traceability, and generate audit-ready discrepancy reports. This reduces a 4-6 week manual audit cycle to continuous, automated validation, freeing expert resources for higher-value analysis.

Inconsistencies between source data, analysis datasets, and submission narratives are a primary source of major deficiencies and information requests from health authorities, leading to costly review delays. This workflow implements cross-agent consistency checking, where agents continuously compare values across clinical data warehouses (e.g., Medidata Rave), statistical computing environments (SAS), and document repositories (Veeva Vault). By surfacing and resolving discrepancies pre-submission, it directly reduces the risk of clinical or statistical review questions that can add 3-6 months to approval timelines.

The final data lock-to-submission timeline is often gated by sequential manual validation steps. This orchestrated workflow runs validation agents in parallel, triggered by data pipeline events. As SDTM datasets are finalized, agents immediately begin mapping to source and forward-tracing to ADaM and tables/listings/figures (TLFs). This parallel processing collapses a traditionally linear 2-3 week validation phase into days, pulling the entire submission milestone forward and providing more buffer for strategic review.

Demonstrating ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate, +) principles for electronic source data is a core inspection requirement. A custom workflow automates the generation of a complete, immutable evidence package. Each validation agent logs its checks, references source system timestamps and user IDs, and outputs a signed report. This creates a continuous, automated audit trail that is inspection-ready, replacing fragmented, manually compiled evidence that is difficult to defend.

As portfolio complexity grows—with more studies, novel endpoints, and global submissions—manual traceability checks do not scale. This agentic architecture treats validation as a configurable, reusable service. New studies or submissions simply define their data maps and rules; the same agent orchestration layer (built on LangGraph or similar) executes the checks. This allows a single operations team to manage validation for an entire pipeline without proportional increases in FTEs, turning a fixed cost into a variable, scalable capability.

Many sponsors rely on expensive, black-box commercial validation tools that are inflexible and create vendor dependency. A custom workflow built on open-source orchestration frameworks (LangChain, Prefect) and integrated directly with your clinical data stack (e.g., Databricks, Snowflake) provides full transparency and control. The implementation cost is offset within 1-2 submissions by eliminating recurring SaaS license fees and reducing the consultant hours typically required to configure and run third-party tools.

This agent connects to electronic data capture (EDC) systems, LIMS, and EHRs to extract raw source records and map them to their initial SDTM variable assignments. It uses semantic understanding to interpret case report form annotations and applies CDISC controlled terminology, creating the foundational traceability link. The agent flags unmapped data points and ambiguous source-to-target relationships for human review, ensuring the integrity of the starting point for ALCOA+ compliance.

Orchestrated via a framework like LangGraph, this agent traverses the defined data flow from SDTM through ADaM to submission tables and figures (TLFs). It executes rule-based checks to verify that derivations, transformations, and statistical analyses preserve the original data intent. It identifies breaks in lineage—such as a patient count in a summary table that cannot be reconciled back to the raw data—and logs each discrepancy with context for the audit trail. This is the core engine for detecting the inconsistencies that trigger regulatory questions.

Upon completion of verification runs, this agent compiles all agent actions, validation results, and flagged issues into a structured, submission-ready traceability report. It generates a defensible explanation layer that connects each data point in the final submission back to its source, complete with timestamps, agent IDs, and rule versions. The report is formatted for direct inclusion in the eCTD or for use during inspections, transforming weeks of manual documentation into an automated, scheduled deliverable.

The workflow's backbone is a custom orchestration layer that connects agents to enterprise systems like Veeva Vault Clinical, SAS, and R/Python environments. It manages state, handles exceptions, and routes tasks—for example, pushing a discrepancy from the verification agent into a Jira queue for a statistical programmer. This layer also interfaces with Regulatory Information Management (RIM) systems to update validation statuses, ensuring submission health dashboards reflect real-time traceability readiness.

Critical for regulated environments, this component defines the approval gates and review protocols. All agent-proposed mappings and discrepancy resolutions are routed through a configured review queue in a system like Veeva Vault or SharePoint. Only a qualified Biostatistician or Data Manager can approve critical overrides. Every action is logged in an immutable audit trail, providing the necessary oversight to satisfy internal quality assurance and external inspector scrutiny.

Practical deployment starts with a single study or SDTM domain as a pilot, connecting to the clinical data warehouse and one statistical computing environment. The architecture is built to be study-agnostic, allowing rollout across the portfolio. Key constraints addressed include data quality variance across studies (requiring agent tuning), exception handling workflows for complex derivations, and integration testing with existing validation scripts to ensure no regression in data quality.