What challenges did AstraZeneca face before AI implementation?

Drug development is complex with disparate data systems and slow insight generation limiting timely clinical trial and regulatory decisions. Manual data compilation across multiple domains caused bottlenecks and inefficiencies in pharmaceutical R&D teams.

What was the impact of the AI solution?

Helped accelerate clinical trial pipelines and medicine development with actionable, trustworthy AI insights. The modular multi-agent architecture increases flexibility and performance of AI applications in pharmaceutical R&D.

Source

Scaled productionEvidence: Medium55/100

AstraZeneca Accelerates Drug Development with Amazon Bedrock Agents

Use case typePatient engagementUpdated Jun 13, 2026

AstraZeneca developed Development Assistant, an AI tool using Amazon Bedrock Agents with multi-agent architecture for fast natural language querying of clinical, regulatory, safety, and quality data. The solution unifies structured and unstructured data sources to provide transparent, actionable insights supporting faster decision-making in drug development. Multi-agent AI architecture routes queries to specialized agents for context-aware, high-performance responses across clinical trial and R&D domains. Development Assistant reduced insight generation time from hours to minutes and scaled to 1,000+ users, breaking down domain silos across pharmaceutical R&D. The tool provides transparent data source referencing and is progressing toward expansion across broader R&D functions to accelerate medicine development pipeline.

Organization: AstraZeneca
Industry: Pharma
Location: United Kingdom
Published: April 2026

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: AstraZeneca
Provider: AWS
Maturity: Scaled Production
Linked source: AWS Customer Stories

Development Assistant reduced insight generation time from hours to minutes and scaled to 1,000+ users, breaking down domain silos across pharmaceutical R&D

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Multi-Agent AI
2Natural Language Querying
3Clinical Trial Insights

Built a multi-agent AI solution using Amazon Bedrock Agents for natural language querying across structured and unstructured datasets.
Implemented a supervisor agent directing queries to specialized subagents optimized for clinical, regulatory, quality, and terminology data.
Combined text-to-SQL generation with retrieval-augmented generation to unify and query heterogeneous data sources rapidly.
Established controlled vocabularies and transparency features to ensure trust and interpretability of AI-generated insights.

Technologies

Amazon Bedrock Agents Amazon Bedrock

Helped accelerate clinical trial pipelines and medicine development with actionable, trustworthy AI insights.
The modular multi-agent architecture increases flexibility and performance of AI applications in pharmaceutical R&D.

Architecture

The architecture employs a multi-agent AI model on Amazon Bedrock Agents, with a supervisor agent routing queries to specialized agents handling clinical, regulatory, safety, quality, and terminology data. It integrates text-to-SQL generation and retrieval-augmented generation to query unified structured and unstructured data. Data sources are harmonized via a Drug Development Data Platform providing standardized, interoperable datasets for AI consumption.

Sources & evidence1

Evidence: Medium55/100Evidence strength