Accelerate regulatory package processing with agentic AI on AWS

Federal agencies process millions of conformity packages annually—documentation that verifies products, services, and federal actions meet specific regulatory standards. Whether it’s certifying a vehicle is safe to drive or confirming a project meets environmental standards, these packages play a vital role. They’re the gatekeepers making sure the public stays safe, the environment is protected, and regulations are followed. Yet the manual processes used to review them have barely evolved in decades. With AI enabled conformity package processing the time to approve the package improved significantly.

The result is predictable: mounting backlogs, extended review timelines, inconsistent determinations, and overburdened staff. Submission volumes continue to grow while staffing remains flat. The complexity and variety of conformity requirements across regulatory domains—spanning certificates of conformance for contract supplies, general certificates of conformity for consumer product safety, environmental compliance documentation, import/export declarations, and technical test reports—compound these challenges further.

Amazon Web Services (AWS) offers a fundamentally different approach. By deploying agentic AI—specialized AI agents that work collaboratively to analyze, validate, and route documentation—federal agencies can dramatically accelerate conformity processing while maintaining or improving quality and compliance standards. The goal isn’t to replace human expertise — it’s to make better use of it. Let AI handle the routine validation work and flag what needs attention, so the experts can spend their time where it matters: making judgment calls, interpreting nuance, and tackling the hard problems.

Rethinking regulatory processing from the ground up

Traditional conformity processing is linear and labor-intensive. A package arrives, sits in a queue, gets assigned to a reviewer, and moves through a series of manual checks. Each check requires the reviewer to locate relevant standards, cross-reference databases, verify completeness, and document their determination. The process works—but it doesn’t scale.

Agentic AI reimagines this workflow as a collaborative system of specialized agents, each responsible for a distinct aspect of the review. Built on Amazon Bedrock and Amazon Bedrock AgentCore, and integrated with Databricks hosted on AWS for data processing and analytics, the system operates like a well-coordinated review team rather than a single overloaded examiner.

The architecture uses the managed gateway and routing capabilities of AgentCore to orchestrate the agent workflow, and Databricks provides the data platform backbone—handling large-scale data extraction from legacy systems, running compliance analytics, and exposing these capabilities to the agents using the Model Context Protocol (MCP). This combination delivers the intelligence of agentic AI with the data engineering power needed to process regulatory packages at scale.

Architecture overview

The solution architecture integrates AgentCore with Databricks to create a comprehensive agent-based processing pipeline. As shown in the following diagram, the design follows a gateway pattern where AgentCore handles agent orchestration, authentication, and memory, and Databricks provides the data processing, extraction, and custom tooling layer.

Figure 1: Agentic AI architecture for regulatory package processing

The flow begins when a user submits a regulatory package through the Agent UI, with the request authenticated using Amazon Cognito. The UI invokes the Strands agent running on AgentCore Runtime, which performs the orchestration, reasoning, and validation. To reach external capabilities, the agent calls AgentCore Gateway, a managed MCP endpoint that aggregates multiple backend targets and exposes them to the agent as a unified set of MCP tools.

AgentCore Gateway routes tool invocations to two target categories:

• Lambda target – A Lambda target backed by AWS Lambda functions executes business processing rules—the regulatory logic that determines which standards apply, what validation checks are required, and how to route edge cases. Independently, the agent uses AgentCore Memory to maintain short-term conversational context and long-term processing knowledge, enabling agents to learn from past determinations and maintain consistency across reviews.
• MCP target – An MCP target pointing at Databricks managed MCP servers (with tool and data access enforced by Unity Catalog) is where the comprehensive solution comes together. Databricks, hosted on AWS, provides the heavy-lifting data capabilities that regulatory package processing demands through its managed MCP servers for AI/BI Genie spaces, Vector Search, Unity Catalog functions, and Genie (natural-language SQL over Databricks SQL warehouses), plus custom MCP servers hosted as Databricks applications for agency-specific tools.

Together these expose data extraction from legacy systems, analytics across historical compliance records, and custom tool integrations that agents can invoke on demand. Databricks Unity Catalog sits alongside this call path as a control-plane governance layer, providing visibility, access policy management, and audit logging across MCP interactions. Amazon Simple Storage Service (Amazon S3) serves as the shared storage layer, holding submitted packages, extracted data, and processing artifacts accessible to both the AgentCore pipeline and Databricks workloads.

Why AgentCore integrated with Databricks

Regulatory package processing is as much a data challenge as it is an AI challenge. Agencies maintain decades of compliance records in legacy systems, structured databases, and document repositories. Making this data accessible to intelligent agents requires a platform that can handle extraction, transformation, and real-time querying at scale.

AgentCore provides the agent runtime: managed infrastructure for deploying, securing, and scaling AI agents with built-in authentication, memory, and tool integration. Databricks provides the data runtime: a unified analytics platform capable of processing massive datasets, connecting to legacy data sources, and serving results back through standard protocols.

The MCP integration between them makes the solution comprehensive. Rather than building point-to-point integrations between each agent and each data source, AgentCore Gateway aggregates the Databricks managed MCP servers and Lambda business rules into a single MCP endpoint. Agents running on AgentCore Runtime can query compliance databases, extract data from legacy objects, invoke custom validation tools, and retrieve historical precedents—all through a single, standards-based protocol. This dramatically simplifies the architecture while maintaining the flexibility to add new data sources and tools without modifying the agent logic.

What the system delivers

At its core, the solution delivers capabilities that transform how agencies handle conformity packages:

• Intelligent document classification – Automatically categorizes incoming documents—certificates, test reports, technical drawings, correspondence—and identifies the applicable regulatory framework without human intervention.
• Multi-source data validation – Enables agents to cross-reference submitted information against internal compliance databases, historical approval records, industry standards repositories, and regulatory requirement catalogs simultaneously—powered by Databricks’s ability to query across disparate data sources in real time. What might take a human reviewer hours of manual lookup happens in seconds.
• Automated compliance checking – Applies business rules through Lambda functions to validate that submissions meet all mandatory requirements—completeness, accuracy, and adherence to specifications—with consistency that human reviewers can’t match across thousands of submissions.
• Legacy data extraction through Databricks MCP servers – Enables agents to reach into existing systems of record—databases, file stores, and legacy applications—without requiring those systems to be rebuilt or migrated. This is critical for agencies with decades of institutional data locked in older platforms.
• Custom tool integration – Allows agency-specific validation logic, external API calls, and specialized processing to be exposed as tools the agents can invoke autonomously, extending the system’s capabilities without modifying the core architecture.

Putting it into practice: Vehicle import conformity

Consider a federal agency that processes thousands of vehicle import applications annually, verifying that imported vehicles conform to Federal Motor Vehicle Safety Standards (FMVSS). The manual process involves reviewing Vehicle Identification Number (VIN) structures, validating manufacturer certifications against approved lists, cross-referencing crash test data and recall databases, and verifying emissions compliance documentation—all before making a conformity determination.

With the AgentCore and Databricks solution, the workflow becomes significantly more streamlined. When a submission comes in through the Agent UI, it’s authenticated via Amazon Cognito. From there, the Strands agent running on AgentCore Runtime calls AgentCore Gateway, which routes the request to a Lambda function. That function applies business rules to figure out which FMVSS standards are relevant for the vehicle class being submitted.

Through the same AgentCore Gateway endpoint, the agent invokes the Databricks managed MCP servers to extract historical data from legacy compliance databases—prior approvals for this manufacturer, recall history for similar vehicle types, and cross-references against known non-conforming patterns.

The agents synthesize these inputs: VIN structure validation against the manufacturer database, completeness checks against required documentation, and anomaly detection comparing this submission to historical patterns. If everything aligns, the system flags the application for expedited approval. If anomalies surface—a VIN structure inconsistent with the claimed manufacturer, or missing crash test documentation, for example—the system escalates to a human expert with a focused summary of exactly what needs attention and why.

The result is a dramatic reduction in review time for straightforward applications, while giving complex cases the human judgment they require.

Agencies positioned to benefit

The pattern applies broadly across the federal government:

• The Environmental Protection Agency can accelerate General Conformity determinations for federal actions affecting air quality, using Databricks to analyze emissions data across State Implementation Plans.
• The Consumer Product Safety Commission can process General Certificates of Conformity more efficiently by validating testing laboratory accreditation against historical databases.
• The Federal Aviation Administration can enhance aircraft conformity inspections by cross-referencing configuration documentation against type certificate databases spanning decades of certifications.
• The Food and Drug Administration can accelerate product registration packages for food, drugs, and medical devices—domains where legacy data in older systems is particularly valuable for pattern detection.
• Customs and Border Protection can expedite import declarations by validating certificates against product classifications and trade agreement requirements in real time.
• The Department of Defense can strengthen supply chain security by cross-referencing contractor certificates against known counterfeit patterns across the defense industrial base.

Each agency shares the same fundamental challenge: high submission volumes, complex regulatory requirements, constrained resources, and critical data locked in legacy systems. The AgentCore and Databricks architecture addresses these challenges simultaneously.

What agencies can expect

Organizations implementing this architecture for conformity processing can expect significant improvements across multiple dimensions. Initial review times can be reduced up to 60–80%, helping agencies clear backlogs and meet statutory deadlines. Processing errors and rework decrease up to 40–50% as automated validation catches inconsistencies that human reviewers might miss during high-volume periods. Throughput capacity increases by 3–5 times without proportional staffing increases.

Perhaps most importantly, the system delivers consistency—standardized application of regulatory requirements across reviewers, regions, and time periods—along with complete transparency through audit trails that show exactly how each determination was reached. Human experts spend their time where it matters most: complex cases, policy interpretation, high-risk determinations, and the kind of nuanced judgment AI can’t replicate.

Implementation considerations

Government deployments require careful attention to several dimensions. Security and privacy are foundational—implementations must use FedRAMP-authorized cloud services with encryption of sensitive data and full compliance with federal information security requirements. AWS GovCloud (US) provides the isolation and compliance controls necessary for these workloads, and Databricks on AWS supports deployment within these boundaries.

Human oversight remains essential. Critical decisions, precedent-setting cases, and enforcement actions must involve human review and approval—AI serves as a decision support tool, not a replacement for human judgment. Systems must also accommodate frequent regulatory updates and evolving compliance requirements without extensive reprogramming, and all AI-generated assessments must be explainable to regulated entities with clear reasoning supporting determinations.

Data quality is foundational to success. The Databricks layer requires clean, well-structured connections to legacy data sources representing the full range of conformity package types and scenarios. Agencies should plan for an initial data integration phase to map existing systems into the MCP-accessible layer.

Conclusion

The future of regulatory compliance is collaborative intelligence: AI agents running on AgentCore handling high-volume, rules-based processing, integrated with Databricks for comprehensive data access, while human experts focus on the work that truly requires their expertise. To explore how this architecture can transform your agency’s conformity processing, visit the companion reference implementation on GitHub, which provides a starting point for an AgentCore deployment with hooks for Databricks integration.

To learn more about building agentic AI solutions for government, explore Amazon Bedrock and Amazon Bedrock AgentCore, or contact your AWS account team to discuss how agentic AI integrated with your existing data platform can accelerate your regulatory workflows.

AWS GitHub Repo – https://github.com/aws-samples/sample-databricks-mcp

About the authors

Figure 2: Author headshot placeholder

Sanjeev Pulapaka is a Principal AI Architect for US Federal Civilian at Amazon Web Services (AWS). He leads the cross-agency initiative shaping how the federal government moves from AI experimentation to mission-critical agentic AI systems—setting technical direction, building governance frameworks, and producing the thought leadership that drives responsible adoption at national scale.

Nikhil Nayar is a Solutions Architect at Amazon Web Services (AWS). He focuses on helping the US federal government accelerate its journey to the cloud and applied AI solutions. Before joining AWS, Nikhil spent 16 years in the field of enterprise architecture and development, working with enterprise customers and service providers.