Scaling Content Review Operations with Multi-Agent Workflow

Introduction

Enterprises today manage large volumes of technical blogs, documentation, knowledge base articles, and compliance content. As cloud platforms evolve rapidly, this content can become obsolete in short timeframes, leading to incorrect guidance, operational risk, and reduced customer trust. Manual content review processes do not scale efficiently and are often unable to keep pace with frequent service updates.

To address this challenge, AWS introduces a scalable, automated approach for content governance using a multi-agent workflow powered by Amazon Bedrock. Instead of relying on a single large language model to interpret and validate content, this architecture decomposes the review process into specialized agents that collaborate. Each agent focuses on a specific responsibility, content scanning, evidence-based verification, or recommendation generation, allowing organizations to scale content review operations with higher accuracy, transparency, and efficiency.

Multi-Agent Workflow

The multi-agent workflow is designed as a structured pipeline where each agent performs a clearly defined task and passes structured outputs to the next stage. This design mirrors real-world editorial review processes, where scanning, fact-checking, and revision are handled as separate steps.

Content Scanner Agent: Intelligent Extraction for Obsolescence Detection

The content scanner agent serves as the entry point to the multi-agent workflow. Its primary responsibility is to identify technical information that is likely to become outdated over time. This includes version numbers, API references, regional availability statements, configuration parameters, pricing details, and prerequisite requirements.

The scanner agent analyzes the content and extracts these technical elements into a structured format. Each element is categorized by type, its location within the content, and its expected time sensitivity. This structured output ensures that downstream agents receive well-organized, machine-readable data, enabling efficient and targeted verification rather than broad, unstructured analysis.

Content Verification Agent: Evidence-Based Validation

The content verification agent receives structured technical elements from the scanner agent and validates them against authoritative sources. This agent uses the AWS documentation MCP server to access the most current AWS technical documentation. Its behavior is guided by prompts that enforce objective and measurable validation criteria rather than subjective reasoning.

The verification agent evaluates each element by checking version-specific information, feature availability across regions or service tiers, syntax accuracy of code or CLI commands, prerequisite validity, and pricing or service limits. For each element, it generates targeted search queries, retrieves current documentation, and compares the original claim with authoritative sources.

Based on this comparison, the agent classifies each element as CURRENT, PARTIALLY_OBSOLETE, or FULLY_OBSOLETE and documents specific discrepancies along with supporting evidence. For example, when validating a claim such as “Amazon Bedrock is available only in us-east-1 and us-west-2,” the agent retrieves current regional availability data and identifies additional supported regions. This structured, evidence-based approach ensures transparency and auditability in automated content review.

Recommendation Agent: Actionable Update Generation

The recommendation agent represents the final stage of the workflow. It transforms verification results into clear, actionable content updates. Rather than merely flagging issues, this agent generates precise recommendations that align with the original content’s tone and structure while correcting inaccuracies.

For partially obsolete elements, the agent suggests targeted edits, such as expanding a list of supported regions or updating version numbers. For fully obsolete elements, it recommends removal or replacement. This enables content teams to apply updates quickly, reducing editorial effort while maintaining consistency and technical accuracy.

Execution Flow with Amazon API Gateway

The content review workflow is typically triggered through Amazon API Gateway, scheduled review jobs, or integrations with content management systems. A request containing the content location is sent to the AgentCore runtime, which initiates a new or existing review session.

The content scanner agent runs first, extracting structured technical elements. These outputs are passed to the verification agent, which validates each element against authoritative AWS documentation. The recommendation agent then generates proposed updates. Final outputs, including classifications and recommendations, can be stored in Amazon S3, routed to human reviewers, or fed back into publishing systems. Throughout the workflow, Amazon Bedrock AgentCore captures logs, metrics, and execution traces for monitoring and governance.

Technical Challenges and Optimizations

At scale, performance and reliability become critical. Large documents should be stored in Amazon S3 and referenced during processing rather than passed directly to agents. Verification agents must rely exclusively on authoritative data sources to minimize hallucinations.

Using modular agents allows independent optimization and prompt tuning. Observability data from Amazon Bedrock AgentCore should be monitored to identify bottlenecks, improve throughput, and ensure consistent performance as content volume increases.

Conclusion

Scaling content review operations requires a structured, evidence-driven approach. By adopting a multi-agent workflow powered by Amazon Bedrock and managed by Amazon Bedrock AgentCore, organizations can automate content scanning, validation, and update generation with high accuracy and transparency.

This architecture enables enterprises to maintain trustworthy, up-to-date content while significantly reducing manual review effort. As generative AI adoption grows, multi-agent systems will become a foundational pattern for managing large-scale content governance challenges.

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