AWS AI agents automate citation validation and formatting to save time and instill confidence

Any organization that receives proposals or issues of publications understands the importance of checking the accuracy and validity of cited sources in those documents. Proposals hinge on the accuracy of this information, yet significant time can be consumed verifying the source’s existence, accuracy, and relevance to the topic. The same can be said about validating scientific article submissions and reviewing student papers, theses, and dissertations. Verifying each type of reference requires a systematic approach to review. Furthermore, although it takes a tremendous amount of time, verification of cited work is crucial to instill confidence in the information presented in the document.

In the current generative AI landscape, models pose a critical citation validation challenge by producing convincing yet potentially fabricated references, inaccurate source attributions, and plausible sounding but false statements that can deceive even experienced reviewers. This makes rigorous verification of AI-generated citations essential to prevent the spread of misinformation disguised as legitimate scholarly work.

The solution

Now you can save time and instill confidence in these documents more quickly by using Amazon Quick Automate, which creates a set of agents to complete complex business tasks with reasoning. Amazon Quick Automate is a powerful multi-agent automation capability within Amazon Quick Suite that streamlines complex enterprise processes spanning multiple departments, systems, and applications. As part of Amazon Quick Suite, Amazon Quick Automate enables organizations to automate end-to-end workflows that involve both UI and API interactions across third-party systems—eliminating the fragmentation and brittleness of traditional automation tools. The service provides robust networking capabilities, including virtual private cloud (VPC) connectivity for accessing private resources and maintaining enterprise-grade security while automating high-judgment workflows with generative AI assistance.

Introducing Amazon Quick Automate

Amazon Quick Automate addresses core enterprise automation by combining the power of generative AI with the comprehensive cloud capabilities of Amazon Web Services (AWS) to transform how organizations automate their business processes. Amazon Quick Automate represents a significant leap forward in enterprise automation by offering:

• AI-powered workflow creation: Organizations can now describe their automation needs using natural language or documentation. Amazon Quick Automate’s planning agent analyzes these inputs and automatically generates detailed workflow plans that can be refined and implemented.
• Adaptive UI automation: The UI agent is Amazon Quick Automate’s native AI agent that leverages a sophisticated Large Action Model (LAM) to perform intelligent web browser automation through natural language instructions. Using advanced in-built Amazon Bedrock and proprietary models, the UI agent can autonomously navigate websites, interact with dynamic user interfaces, and adapt in real-time to layout changes or unexpected scenarios—dramatically reducing maintenance overhead while improving reliability compared to traditional automation tools. Simply provide natural language instructions, and the UI agent will intelligently perform complex browser actions including clicking, typing, data extraction, and generating structured outputs optimized for downstream automation workflows—making it ideal for tasks like summarizing webpage content or fetching data through dynamic website navigation. Key capabilities include:
  • Intelligent interface interaction: Understands and adapts to changing UI layouts automatically
  • Natural language control: Execute complex browser tasks using simple written instructions
  • Structured data output: Generate organized, structured data optimized for integration with other automation steps
  • Real-time adaptation: Handles unexpected scenarios and interface changes without manual reconfiguration
  • Comprehensive browser actions: Navigate, click, type, read, and extract data across any web interface
• Unified automation ecosystem: Unlike traditional solutions that need multiple tools, Amazon Quick Automate provides a single, comprehensive platform that combines UI automation, API integrations, and workflow orchestration to streamline management and reduces costs.

The service introduces several transformative capabilities:

• Intelligent planning: A proprietary multi-modal large language model (LLM) analyzes process requirements and automatically generates optimized workflow plans that can be modified to create automation following best practices.
• Dynamic execution: Advanced AI agents, such as the UI agent, custom agent and integrated Amazon Bedrock agents that can handle complex decision-making and adapt to changing conditions in the user interface during workflow execution.
• Smart human integration: Seamless human-in-the-loop capabilities provide appropriate oversight while maximizing automation efficiency.
• Enterprise-grade governance: Built-in controls provide proper access management and data security across all automated processes.

Combining these capabilities with the serverless infrastructure and pay-per-use pricing model of Amazon Quick Automate makes sophisticated automation accessible to organizations of all sizes while significantly reducing total cost of ownership.

Amazon Quick Automate creates intelligent agentic automation that performs multiple validation tasks in parallel using UI and custom agents for reasoning, integrating with external academic storage, websites and search engines, while enabling customers to maintain citation compliance standards. As a result, organizations can automate high volumes of transactions and complex workflows that span multiple applications and systems with reliability and accuracy.

Example solution

The following use case leverages Amazon Quick Automate to validate citations by checking the American Psychological Association (APA) format compliance, verifying citation links, and extracting metadata from academic papers. The system uses Amazon Bedrock for AI-powered validation and implements human-in-the-loop verification for quality assurance.

Solution architecture

The following figure shows the solution architecture.

Figure 1. Architectural diagram of the Citation Validation Solution described in this post. AWS Transfer Family, Amazon Simple Email Service (Amazon SES), Amazon S3, Amazon Bedrock, Amazon Quick Automate, LAMs, and Amazon Bedrock LLMs

Key workflow steps

1. Document ingestion

The citation validation process begins with a flexible ingestion system that accommodates multiple input channels. Organizations can upload citations through Secure File Transfer Protocol (SFTP) connections using AWS Transfer Family, while individual providers can submit citations directly via email using Amazon Simple Email Service (Amazon SES). All incoming documents are automatically routed to a designated Amazon Simple Storage Service (Amazon S3) bucket, where they undergo initial processing to extract individual citations and create structured records for validation.

2. AI-powered APA format validation

AWS uses the diverse set of foundation models (FMs) in Amazon Bedrock to perform sophisticated APA format validation. In this use case, the Claude 4.5 Sonnet  model was chosen. However, users have the flexibility to choose from a wide range of models depending on their specific use case. The system uses the model to automatically analyze each citation’s structure, generating detailed compliance scores and identifying potential formatting issues. When citations don’t meet the necessary confidence threshold, the system seamlessly integrates with human-in-the-loop functionality—creating targeted review tasks for expert validation. When the human reviews, confirms, and provides the recommendation back asynchronously, Amazon Quick Automate completes the remaining steps.

3. Citation link verification 

The UI agent in Amazon Quick Automate, powered by LAMs (large action models), and Amazon Bedrock LLMs (large language models), implements a robust, multi-layered approach to link verification. The system first attempts to validate citations through their provided URLs. In this solution, we opted for PubMed as a secondary validation source. If the direct link is inaccessible, then the solution automatically searches PubMed’s public website for validation using the agent’s reasoning capabilities. Upon successful navigation to either source, the system methodically extracts the following essential bibliographic data:

• Journal name
• Volume information
• Publication date
• Page numbers
• Article titles
• Author names

4. Data validation

The data verification process represents a sophisticated application of AI-powered text analysis powered by Amazon Bedrock LLM models to validate data (for example, author identities) across citations. At its core, the system employs intelligent pattern recognition to handle the complexities of academic representations from abbreviated formats (for example, full names to abbreviated names). The verification engine applies a series of matching algorithms that look beyond surface-level text, considering common variations in presentations while maintaining accuracy (for example, name identification).

5. Data processing

The final stage of the workflow brings together all validation outcomes into a comprehensive results management system. The solution consolidates findings from each validation checkpoint—such as format compliance scores, link verification status, and author matching results—into a structured output format. These results are securely stored in Amazon S3, which maintains a complete audit trail of all validation activities. The system automatically generates notifications for relevant stakeholders, making sure of transparent and efficient communication of validation outcomes.

6. Governance

Amazon Quick Automate implements a multi-layered governance framework that enforces strict access controls and process oversight. The automation uses  AWS Identity and Access Management (IAM) roles with least-privilege principles, restricting Amazon S3 bucket access to specific paths within the bucket for both input retrieval and output storage. The Amazon Bedrock interactions are controlled through dedicated service roles, limiting access to specific models and predetermined prompt patterns. IAM policies govern the browser agent’s activities and define permissible navigation patterns and data extraction boundaries. Human-in-the-loop tasks are managed through a role-based queue system, which tracks task assignments and resolutions with clear ownership and audit trails. The workflow maintains segregation of duties between teams using automation groups.

Benefits for citation validation and formatting

Amazon Quick Automate provides end-to-end automation support through both agentic (AI-powered, adaptive) and deterministic (rule-based, predictable) approaches. This makes sure that the right automation method is applied to each task within a workflow.

The following are four aspects of the end-to-end process automation and orchestration:

• Authoring studio: This features an intuitive interface where users can create and manage workflows using AI-powered planning agents. The studio facilitates natural language inputs and collaborative workflow development, streamlining the automation creation process by reducing time to build intelligent automations.

• Fully managed service: This operates as a serverless, AWS-managed service, eliminating the need for infrastructure management and maintenance while making sure of optimal performance and reliability.

• Governance and responsible AI: This implements enterprise-grade security controls with comprehensive role management, governance frameworks, and complete audit trails. It provides responsible AI usage with built-in safeguards and monitoring capabilities.

• Consumption based pricing: This uses a flexible pricing model tied directly to process execution, which allows organizations to pay only for what they use without upfront commitments or fixed costs.

Proof of concept

The following screenshots showcase the solution in Amazon Quick Automate with input and output.

Figure 2. Input – List of citations and S3 bucket

Figure 3. Automation project – Project page in Amazon Quick Automate

Figure 4. Automation project – Authoring canvas (Low code/no code) and Build with Assistant

Figure 5. Execution – UI streaming, logs and audit trail, human in the loop task creation

Figure 6. Execution – Agent thinking in audit trail

Figure 7. Human in the loop task forms for manual verification and transparency

Figure 8. Output with status and comments

Curious to learn more?

To learn more about Amazon Quick Automate, visit this webpage.

To learn more about Amazon Quick Suite, visit this webpage.