Scaling content review operations with multi-agent workflow

Enterprises are managing increasing amounts of content, from product catalogs and support articles to knowledge bases and technical documentation. Ensuring that this information remains accurate, relevant and aligned with the latest business facts is a difficult challenge. Manual content review processes are often slow, expensive, and unable to keep pace with dynamic business needs. according to a McKinsey studyOrganizations that use generic AI for knowledge tasks, including content review and quality assurance, can increase productivity by 30-50% and dramatically reduce time spent on repetitive verification tasks. Similarly, Research from Deloitte This highlights that AI-powered content operations not only increases efficiency but also helps organizations maintain high content accuracy and reduce operational risk.

Amazon Bedrock AgentCore, a purpose-built infrastructure for deploying and operating AI agents at scale strands agentAn open source SDK for building AI agents, empowering organizations to automate comprehensive content review workflows. This agent-based approach enables businesses to evaluate content for accuracy, verify information against authoritative sources, and generate actionable recommendations for improvement. By using specialized agents working together autonomously, human experts can focus on strategic review tasks while the AI agent system handles large-scale content verification.

The agent-based approach we present applies to any type of enterprise content, from product documentation and knowledge bases to marketing materials and technical specifications. To put these concepts into action, let’s walk through a practical example of reviewing blog content for technical accuracy. These patterns and techniques can be directly adapted to different content review needs by adjusting agent configuration, tools, and validation sources.

solution overview

Implements content review solutions Multi-Agent Workflow pattern, where three specialized AI agents built with Strands agents and deployed on Amazon Bedrock AgentCore work in a coordinated pipeline. Each agent receives the output from the previous agent, processes it according to its particular task, and sends the rich information to the next agent in the sequence. This creates a progressive refinement process where:

content scanner agent Analyzes raw material and extracts relevant information
Content Verification Agent Takes these extracted elements and validates them against authoritative sources
recommendation agent Turns validation findings into actionable content updates

Technical content maintenance requires many specialized agents because manually scanning, verifying, and updating documents is inefficient and error prone. Each agent has a focused role – the scanner identifies time-sensitive elements, the verifier checks current accuracy, and the recommendation agent produces accurate updates. The system’s modular design, with clear interfaces and responsibilities, makes it easy to add new agents or expand capabilities as content complexity increases. To explain how this agent-based content review system works in practice, we walk through an implementation that reviews technical blog posts for accuracy. Tech companies often publish blog posts detailing new features, updates, and best practices. However, the rapid pace of innovation means that some features become obsolete or updated, making it challenging to keep information up to date across hundreds or thousands of published posts. While we demonstrate this pattern with blog content, the architecture is content agnostic and supports any content type by configuring agents with the appropriate prompts, tools, and data sources.

Practical Example: Blog Content Review Solution

We use three special agents that communicate sequentially to automatically review posts and identify outdated technical information. Users can trigger the system manually or schedule it to run periodically.

Figure-1 Blog Content Review Architecture

The workflow begins when a blog URL is provided to the blog scanner agent, which retrieves the content using Strands. http_request Extracts key technical claims requiring equipment and verification. The verification agent then queries AWS Documentation MCP Server To fetch the latest documentation and verify technical claims against the current documentation. Finally, the recommendation agent synthesizes the findings and produces a comprehensive review report with actionable recommendations for the blog team.

The code is open source and hosted on GitHub.

Multi-Agent Workflow

Content Scanner Agent: Intelligent Extraction for Obsolescence Detection

The Content Scanner agent serves as the entry point into a multi-agent workflow. It is responsible for identifying potentially obsolete technical information. This agent specifically targets elements that are likely to age over time. The agent analyzes the content and produces structured output that categorizes each technical element by type, location in the blog, and time-sensitivity. This structured format enables the verification agent to receive well-organized data that it can process efficiently.

Content Verification Agent: Evidence-Based Verification

The content validation agent receives structured technical elements from the scanner agent and performs validation against authoritative sources. The Validation Agent uses the AWS Document MCP Server to access current technical documentation. For each technical element received from the scanner agent, it follows a systematic verification process guided by specific signals that focus on objective, measurable criteria.

The agent is asked to check:

Version-specific information: Does the mentioned version number, API endpoint, or configuration parameter still exist?
facility availability: Is the described service feature still available in the specified areas or tiers?
syntax accuracy: Do the code examples, CLI commands, or configuration snippets match the current documentation?
prerequisite validity: Are the listed requirements, dependencies, or setup steps still accurate?
Pricing and Limitations: Do the stated costs, quotas or service limits align with current published information?

For each technical element received from the scanner agent, the agent performs the following steps:

Generates targeted search queries based on element type and content
Query the Documentation Server for current information
Compares the original claim against official sources using the specific criteria above
The validation classifies the result as CURRENT, PARTIALLY_OBSOLETEOr FULLY_OBSOLETE
Document specific discrepancies with evidence

Example validation in action: When the Scanner Agent identifies the claim “Amazon Bedrock is available only in US-East-1 and US-West-2 regions,” the Verification Agent generates the search query “Amazon Bedrock available regions” and retrieves the current regional availability from the AWS document. Recognizing that Bedrock is now available in 8+ regions, including EU-West-1 and AP-Southeast-1, it classifies it as PARTIALLY_OBSOLETE With evidence: “The original claim lists 2 regions, but the current document shows availability in US-East-1, US-West-2, EU-West-1, AP-Southeast-1, and 4 additional regions as of the verification date.”

The output of the validation agent maintains the element structure from the scanner agent while adding these validation statements and evidence-based classifications.

Recommendation Agent: Actionable Update Generation

The recommendation agent represents the final step in the multi-agent workflow, transforming validation findings into content updates ready for implementation. This agent receives the validation results and generates specific recommendations that maintain the style of the original content while correcting technical inaccuracies.

Adopting the Multi-Agent Workflow Pattern for Your Content Review Use Cases

The multi-agent workflow pattern can be quickly adapted to any content review scenario without architectural changes. Whether reviewing product documentation, marketing materials, or regulatory compliance documents, the same three agent sequential workflow applies. This requires modifying system cues to focus on domain specific elements for each agent and potentially changing tools or knowledge sources. For example, while our blog review example uses a http_request Tools to fetch blog content and AWS documentation MCP servers for validation, a product catalog review system can use database connector tools to retrieve product information and query inventory management APIs for validation. Similarly, a compliance review system would adjust the scanner agent’s prompt to identify regulatory statements rather than technical claims, link the validation agent to legal databases rather than technical documentation, and configure the recommendation agent to generate audit-ready reports rather than content updates. The core sequential steps extraction, validation, and recommendation remain constant across all of these scenarios, providing a proven pattern that scales from technical blogs to any enterprise content type. We recommend the following changes to adapt the solution for other content types.

change the value of CONTENT_SCANNER_PROMPT, CONTENT_VERIFICATION_PROMPTAnd RECOMMENDATION_PROMPT Variables with your custom prompt instructions:

  python
  CONTENT_SCANNER_PROMPT = """"""
  CONTENT_VERIFICATION_PROMPT = """"""
  RECOMMENDATION_PROMPT = """"""

Official documentation for the Content Verification Agent Update MCP Server:

  python
   product_db_mcp_client = MCPClient(
       lambda: stdio_client(StdioServerParameters(
           command="uvx", args=("")
       ))
   )

Add appropriate content access tools such as database_query_toolAnd cms_api_tool When to content scanner agent http_request Equipment is inadequate:

python
   scanner_agent = Agent(
       model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
       system_prompt=CONTENT_SCANNER_PROMPT,
       tools=(database_query_tool, cms_api_tool)  # Replace http_request
   )

These targeted modifications enable the same architectural pattern to handle any content type while maintaining the proven three-agent workflow structure, ensuring reliability and consistency across different content domains without requiring changes to the core orchestration logic.

Conclusion and next steps

In this post, we explained how to build an AI agent driven content review system using Amazon Bedrock AgentCore and Strands Agents. We demonstrated a multi-agent workflow pattern where specialized agents work together to scan content, verify technical accuracy against authoritative sources, and generate actionable recommendations. Additionally, we discussed how to adapt this multi-agent pattern to different content types by modifying agent prompts, tools, and data sources while maintaining the same architectural framework.

We encourage you to test the sample code available GitHub into your account to get hands-on experience with the solution. As a next step, consider starting a pilot project on a subset of your content, customizing agent prompts for your specific domain, and integrating verification sources appropriate for your use case. The modular nature of this architecture allows you to iteratively refine each agent’s capabilities while expanding the system to handle the full content review needs of your organization.

About the authors

Sarath Krishnan is a Senior General AI/ML Specialist Solutions Architect at Amazon Web Services, where he helps enterprise customers design and deploy generative AI and machine learning solutions that deliver measurable business results. He brings deep expertise in generative AI, machine learning, and MLOps to build scalable, secure, and production-ready AI systems.

Santosh Kuriakose is an AI/ML Specialist Solutions Architect at Amazon Web Services, where he leverages his expertise in AI and ML to create technology solutions that deliver strategic business outcomes for his customers.

Ravi Vijayan Customer Solutions Manager at Amazon Web Services. He has expertise as a developer, tech program manager, and client partner, and is currently focused on helping clients fully realize the potential and benefits of migrating to the cloud and modernizing with Generative AI.