How Leidos enhanced intelligent document processing using agentic AI on AWS

When government agencies process millions of documents monthly—each with unique compliance requirements—traditional intelligent document processing (IDP) pipelines hit a wall. Leidos faced exactly this challenge with their ManagedX platform, an AI-powered, cloud-based IDP solution built on Amazon Web Services (AWS).

This post shares how Leidos enhanced ManagedX by adopting multi-agent workflow patterns using the open source Strands Agents SDK, and how AWS Enterprise Support helped guide that evolution.

The challenge: Scaling document processing for diverse government needs

ManagedX was already automating the extraction and processing of structured, semi-structured, and unstructured documents using AI and machine learning (ML). But as Leidos expanded across government agencies—each with distinct document types and compliance requirements—the team identified limitations in the existing architecture.

The core challenge was flexibility. Some agencies process thousands of short forms daily; others analyze single documents spanning hundreds of pages. The existing pipeline made it difficult to scale individual capabilities independently or offer a modular, pay-per-component model.

“Our government customers don’t just process one type of document—a single case file might include medical records, legal briefs, financial forms, and handwritten notes—all requiring different extraction strategies and compliance rules,” said Bill Zhou, Cloud DevOps Engineer at Leidos.

“In our previous setup, introducing a new document type or agency-specific workflow meant modifying the core pipeline, which slowed our ability to onboard new missions. We needed a flexible system where each processing capability—optical character recognition (OCR), classification, extraction, validation—could evolve independently and be composed differently for each agency’s needs,” said Justin Miles, Systems Engineer at Leidos.

“We were spending more time adapting the pipeline for each new agency than actually improving our AI capabilities. The multi-agent approach lets us compose processing workflows like building blocks—each agent is specialized, testable, and reusable across missions.” Bill Zhou, Cloud DevOps Engineer at Leidos.

Leidos needed a modular architecture that could adapt to each agency’s requirements without rebuilding the pipeline for every new use case.

Why Strands Agents SDK and the workflow pattern

Rather than iterating on the existing monolithic pipeline, Leidos adopted a multi-agent architecture—decomposing the document processing workflow into specialized agents, each responsible for a distinct task: OCR, text extraction, document classification, field extraction (structured data pull), validation, and graph/search indexing. This architecture powered a variety of use cases, from healthcare claims processing, legal e-discovery, and financial documentation to insurance underwriting. In every instance, auditability and deterministic ordering mattered in the regulated environments, which helped Leidos establish efficient workflow patterns.

The Strands Agents SDK—an open source, Apache 2.0-licensed framework already powering production features in AWS services—proved to be a natural fit for several reasons.

First, Strands supports a workflow agent pattern designed for sequential, deterministic processing with explicit dependency management. For government document processing, where the sequence matters—classify, then extract, then validate, then store—this determinism provides the auditability and consistency that regulated environments require. Leidos evaluated collaborative and swarm patterns, where agents negotiate dynamically, but chose the workflow pattern because predictable execution was non-negotiable.

Second, Strands’s Python-native tool definitions made it possible for Leidos to wrap existing capabilities as tools using simple decorators, without rewriting them. Each agent received its own tailored toolset, keeping responsibilities cleanly separated.

Third, Strands’s built-in support for Model Context Protocol (MCP) servers simplified how agents connected to AWS services. Rather than building custom integrations for each service, MCP provided pre-built tool interfaces—reducing development effort and making it simpler to add new service connections as the platform evolves.

Finally, Strands is model-agnostic, so Leidos can assign different Amazon Bedrock foundation models to different agents based on task complexity. Lighter models handle classification; more capable models handle validation with personally identifiable information (PII) guardrails powered by Amazon Bedrock Guardrails. Each model operates with separate throughput quotas, improving both cost-efficiency and performance isolation. Strands also provides built-in observability, logging every tool execution with its parameters—a critical capability for the auditability that government document processing demands.

With the architecture pattern selected, the next challenge was deployment in a regulated environment.

Deploying multi-agent AI in AWS GovCloud with containers

Deploying agentic AI in AWS GovCloud (US) presented its own constraints. At the time of implementation, managed agent orchestration services were not yet available in the AWS Region, so Leidos needed an alternative approach to run and scale their multi-agent system.

Because Strands is lightweight and runs anywhere Python runs, the team chose a container-based deployment using AWS Fargate. This container-based pattern proved to be a practical blueprint for running multi-agent workloads in regulated environments where managed services might not yet be available.

The role of AWS Enterprise Support

Throughout this evolution, Leidos worked with their AWS Technical Account Manager (TAM) through AWS Enterprise Support. Rather than prescribing a solution, the TAM served as a strategic advisor—helping the team evaluate agent patterns, navigate service constraints in GovCloud, and think through trade-offs between deployment approaches.

This type of engagement reflects a broader shift within AWS Enterprise Support toward proactive, strategic guidance—helping customers connect emerging capabilities like agentic AI with specific mission requirements.

The engagement was mutually beneficial for Leidos, who decided to build on AWS due to a combination of customer alignment, platform maturity, and developer velocity. With many of its government customers heavily invested in AWS—particularly within GovCloud—Leidos worked within their existing AWS footprint while maintaining compliance and security requirements.

“Ultimately, it was AWS’s flexibility, scalability, and partnership support that helped to bring ManagedX to life and allowed us to deliver solutions quickly and effectively for our customers,” said Zhou.

Lessons learned

For organizations considering a similar path, Leidos’s experience offers several takeaways:

• Match the agent pattern to the processing sequence – If your document workflow follows a predictable path, the workflow pattern in Strands provides the determinism and auditability that government environments require.
• Use existing code as tools – Strands’s decorator-based tool definitions and MCP support mean you don’t have to rewrite working logic—wrap it and assign it to the right agent.
• Plan for Regional constraints – In regulated environments like GovCloud, not every managed service is available on day one. Strands’s lightweight footprint makes container-based orchestration with Fargate and Amazon SQS a viable alternative.
• Engage your support relationship early – AWS Enterprise Support TAMs can help evaluate emerging patterns and navigate constraints—engaging them early in the architectural decision process can save significant time and rework.

Conclusion

Leidos’s enhancement of ManagedX demonstrates how an established IDP platform can evolve to meet growing government demands through multi-agent AI patterns. By selecting the Strands Agents SDK for its workflow capabilities, built-in tooling support, and model flexibility—and deploying on containers in GovCloud—Leidos built a modular, scalable solution that adapts to diverse agency needs.

“The modular architecture significantly improved ManagedX’s onboarding speed and processing efficiency, allowing us to establish new agency workflows much faster,” said Miles.

“ManagedX started as a document processing platform, but the agent architecture unlocked something bigger—it’s quickly becoming an intelligent case management system where AI plays an important role from intake to final recommendations. And with our foundation in place, we’re excited to introduce advanced capabilities like multi-level case intelligence, automated artifact generation, and a conversational AI interface that will help us boost insight and decision-making for case workers,” said Zhou.

For other public sector organizations exploring agentic AI, the path doesn’t require starting from scratch. It starts with understanding your processing requirements, choosing the right agent pattern, and engaging strategic partners like AWS Enterprise Support to help guide the journey.

To learn more about how AWS Enterprise Support can help your organization accelerate innovation, contact your AWS account team or visit the Enterprise Support homepage. To learn more about building with the Strands Agents SDK, visit the Strands Agents documentation. Learn more about Managed X and speak with a member of the ManagedX team.