Harnessing Agentic AI for Aggregate Reporting: A Game-Changer for Compliance

In the highly regulated world of pharmacovigilance and life sciences, aggregate reporting plays a pivotal role in maintaining public health and ensuring regulatory compliance. With increasing data volumes, tighter deadlines, and growing complexity in regulatory requirements, traditional methods of preparing and submitting aggregate safety reports are proving to be insufficient. Enter Agentic AI—a transformative approach that redefines automation through autonomy, reasoning, and proactive decision-making.

This blog explores how Agentic AI is revolutionizing aggregate reporting, streamlining compliance processes, and empowering pharmacovigilance teams to meet stringent regulatory expectations with unprecedented efficiency and accuracy.

What is Aggregate Reporting?

Aggregate reporting involves the periodic compilation, analysis, and submission of safety data for marketed or investigational drugs. Key report types include:

• Periodic Safety Update Reports (PSURs)

• Development Safety Update Reports (DSURs)

• Periodic Benefit-Risk Evaluation Reports (PBRERs)

• Annual Safety Reports (ASRs)

These reports aggregate adverse event data across geographies and timelines to evaluate the benefit-risk profile of a product. Regulatory authorities, such as the FDA, EMA, and MHRA, use these reports to assess whether a product remains safe for continued use.

Given the sheer volume and complexity of safety data from spontaneous reporting systems, clinical trials, literature, and real-world sources, generating these reports is a resource-intensive process requiring high-level data integration, signal evaluation, narrative development, and expert oversight.

The Compliance Challenge

Pharmaceutical companies face mounting pressure to:

• Ensure timely submission of aggregate reports.

• Maintain consistency across multiple data sources and formats.

• Continuously monitor benefit-risk balance.

• Comply with evolving global regulations (e.g., ICH E2E guidelines).

Traditional methods, even when supplemented with standard automation, often fall short. They rely heavily on manual data collation, human validation, and siloed systems that create inefficiencies and risk of non-compliance.

The key pain points include:

• Time constraints: Preparation of PSURs or DSURs can take weeks to months.

• Manual errors: High risk of data entry errors, inconsistent formats, and missed signals.

• Resource burden: Requires cross-functional coordination among safety, regulatory, clinical, and data teams.

• Inflexibility: Difficult to adapt quickly to changing regulations or ad hoc reporting requests.

These challenges call for a smarter, more autonomous solution—and Agentic AI delivers exactly that.

Understanding Agentic AI

Agentic AI refers to artificial intelligence systems that operate with a level of autonomy, goal orientation, and adaptability. Unlike traditional AI models that rely on rigid algorithms or require constant human prompts, Agentic AI agents are capable of:

• Planning: Breaking down complex tasks into manageable subtasks.

• Reasoning: Making decisions based on context, rules, and desired outcomes.

• Learning: Adapting based on feedback and new data inputs.

• Acting autonomously: Executing tasks with minimal human oversight.

In the context of aggregate reporting, Agentic AI can be trained on pharmacovigilance guidelines, historical report data, and product-specific safety profiles to generate, refine, and submit reports in a compliant and efficient manner.

Key Applications of Agentic AI in Aggregate Reporting

Here’s how Agentic AI is transforming aggregate reporting workflows:

1. Automated Data Aggregation and Integration

Agentic AI can autonomously pull data from disparate sources—spontaneous reports, literature databases, EHRs, clinical trial systems—and harmonize it in real time. It understands the data requirements for different report types and aligns inputs accordingly.

Benefits:

• Saves hundreds of hours of manual data collection.

• Reduces risk of missing data points.

• Ensures consistency across sources.

2. Dynamic Signal Evaluation and Risk Analysis

Traditional signal detection is often reactive. Agentic AI, however, can proactively monitor safety signals by applying causal inference models, disproportionality analysis, and historical comparisons. It can flag emerging risks and suggest when additional data or analysis is warranted.

Benefits:

• Enhanced early warning system for safety concerns.

• More robust benefit-risk assessments.

• Supports proactive regulatory engagement.

3. Natural Language Generation for Narratives

One of the most time-consuming parts of aggregate reporting is drafting clinical narratives and risk evaluations. Agentic AI models equipped with natural language generation (NLG) capabilities can create medically accurate, regulation-aligned narrative content.

Benefits:

• Rapid generation of high-quality report narratives.

• Consistency in language, tone, and terminology.

• Reduces burden on medical writers and safety physicians.

4. Version Control and Audit Trail Management

Agentic AI maintains a complete version history of reports, with timestamped changes and rationales. It can also cross-reference past reports to maintain continuity and flag discrepancies.

Benefits:

• Simplifies audits and inspections.

• Enhances traceability and transparency.

• Minimizes regulatory risk.

5. Submission-Ready Compliance Checks

Before submission, Agentic AI can perform automated validations to ensure all sections are complete, references are cited, appendices are attached, and formats meet regulatory standards. It can even generate submission checklists and QC documentation.

Benefits:

• Fewer rejected or delayed submissions.

• Confidence in regulatory readiness.

• Reduced manual quality control workload.

Real-World Impact: Case Study Examples

Pharmaceutical Company A: Accelerated PSUR Generation

By implementing Agentic AI into its aggregate reporting workflow, Company A reduced its PSUR preparation time from 10 weeks to 3 weeks. The AI agent handled 80% of data collation and 60% of narrative drafting, freeing safety physicians to focus on clinical interpretation.

Biotech Startup B: Regulatory Inspection Preparedness

Startup B used Agentic AI to automate DSUR compilation. During a regulatory inspection, the AI-generated audit trail and rationale documentation passed scrutiny with zero findings—demonstrating how Agentic AI not only streamlines reporting but also strengthens compliance.

uidelines (e.g., ICH, FDA, EMA) to ensure ongoing alignment with changing standards.

Considerations for Implementation

While Agentic AI offers immense promise, organizations must consider:

• Data governance: Ensure high-quality, structured data inputs.

• Regulatory validation: AI-generated content must be auditable and meet regulatory requirements.

• Human-in-the-loop: Safety experts must oversee and validate critical decision points.

• Change management: Invest in training and stakeholder alignment to maximize adoption.

The Future: Towards Autonomous Pharmacovigilance

Agentic AI is not just an enabler—it is a catalyst for a new era of autonomous pharmacovigilance. As these systems become more sophisticated, we can envision a future where AI agents:

• Continuously monitor global safety data.

• Auto-generate and submit aggregate reports.

• Communicate with regulatory portals.

• Learn from past regulatory feedback to improve future reports.

This vision not only reduces compliance risk but also empowers pharmacovigilance teams to focus on higher-order tasks—such as strategic risk mitigation, scientific exploration, and patient advocacy.

Conclusion

Aggregate reporting is a cornerstone of drug safety and regulatory compliance. With increasing pressure on timelines, accuracy, and global coordination, traditional methods are no longer sufficient. Agentic AI offers a revolutionary path forward—combining autonomy, intelligence, and agility to streamline reporting, enhance compliance, and ultimately protect patient safety.

By embracing Agentic AI, life sciences organizations can turn regulatory reporting from a burdensome obligation into a strategic advantage.