Managing Sustainability Data for Digital Product Passports with Agentic AI

Digital Product Passports (DPPs) are set to transform the landscape of product transparency, regulatory compliance, and supply chain sustainability—yet organizations face challenges integrating data, verifying information, and adapting to evolving rules. In this post, we explore how we see AWS customers address the topic of DPPs and the role agentic AI plays in automating and improving DPP processes, from supplier data harmonization to automated compliance checks. We introduce Sustainability Data Fabric (SDF) as foundational building block for managing DPPs along the lifecycle of manufactured products, using Amazon Bedrock AgentCore to accelerate a production-ready implementation.

Introduction

Imagine when you purchase a textile product or electronic device with a battery, it comes with a scannable QR code that allows for instant access to product information including materials, carbon footprint, repairability, and end-of-life instructions. This is the vision behind the EU’s Digital Product Passport initiative, established under the Ecodesign for Sustainable Products Regulation (ESPR). It mandates standardized, product-level data to improve traceability, compliance, and circularity across priority sectors including batteries, textiles, electronics, furniture, and construction materials. The regulation phases in requirements starting in 2027 with battery passports and is expanding through 2030 for additional product categories. Core DPP attributes include:

1. Identity data: Manufacturer information, product serialization, batch tracking
2. Materials and chemicals: Composition, hazardous substances, material provenance
3. Performance and safety: Product specifications, certifications, test results
4. Repairability and end-of-life: Disassembly instructions, spare parts availability, recycling pathways
5. Carbon footprint: Lifecycle emissions across raw materials, manufacturing, distribution, and disposal
6. Supply chain due diligence: Supplier verification, ethical sourcing documentation

Figure 1: DPP mock-up of a residential building

Access to DPP data is tiered. Consumers and regulators view transparency information, while sensitive commercial details remain permissioned to authorized stakeholders. This creates governance requirements that must be built into the technical architecture from day one. Efficient data collection and exchange rely on interoperable schemas and secure data sharing using standardized APIs and modern architecture patterns such as data spaces. Agentic AI accelerates supplier onboarding and data normalization, continually reconciles and validates inputs from business applications and multi-tier partners, and automates lifecycle updates and governance. This helps enterprises meet compliance at scale while improving supply chain transparency, carbon reporting, and customer‑facing experiences.

Business Value from Digital Product Passport Investment

While regulatory compliance is one aspect, especially in the EU, it is not the only reason why customers across industries making physical goods are considering DPP implementations, sometimes using different terms like Digital Material Passports (DMPs). According to research commissioned by Amazon and conducted by Oxera, DPPs have the potential to transform product information sharing, delivering cost efficiencies and new business opportunities. Chemical manufacturers are creating passport capabilities for materials to handle an increasing volume of transparency requests received from their customers in a scalable, future-proof manner. Approaches to operational data, like Digital Thread in manufacturing, aspire the enablement of analytics and AI use cases based on holistic collection and contextualization of product attributes along the entire value chain. Finally, transparency into sustainability attributes of products allows companies to compete not only on monetary cost, but on carbon footprint, while allowing the end customer to validate this directly.

Automating compliance from both the regulatory and customer transparency perspective allows enterprises to focus on their core business while meeting regulatory expectations. Oxera estimates DPPs will reduce compliance costs in consumer electronics by 15%, saving around €200 million per year. However, setting up the capabilities necessary for DPP is no easy task. Organizations must collect and harmonize data from hundreds or thousands of suppliers, each with varying levels of digital maturity. They must integrate legacy systems, validate data accuracy, and adapt to industry standards that continue to evolve. At a point, manual approaches no longer scale.

Customer Challenges and Foundational Building Blocks

Implementation challenges for DPPs are summarized into five areas:

1. Supply chain data collection and harmonization: Variations in digital maturity, data formats, and protocols make reliable data collection from a global supplier network labor-intensive and prone to errors.
2. System integration and interoperability: Data integration involves legacy systems and siloed business applications, that lack modern interfaces making it difficult to extract and consolidate data across systems and domains.
3. Data validation, security, and privacy: Sensitive product information must be handled confidentially, ensuring data governance, auditability, and access controls where errors pose organizational and financial risk.
4. Regulatory and compliance adaptability: With DPP requirements evolving across industries and regions, organizations need to continuously adapt workflows and data structures to remain compliant.
5. Operational scalability and cost: Manual processes for creating and updating DPPs become unsustainable at scale, increasing operational costs.

A combination of data management and governance, data spaces for secure and interoperable data sharing, and agentic systems for data harmonization and verification, as well as orchestration of complex, non-deterministic tasks, allow for automating areas of DPP creation and maintenance over time.

Agentic AI systems—intelligent agents that make context-aware decisions, and coordinate with other agents—offer a transformative approach since they understand intent and adapt to changing conditions. Agents monitor standards and policy changes and perform continuous compliance checks and data collection. They consider third-party information, such as environmental data sets or academic research, for ongoing validation and plausibility checks of attributes included in DPPs. For in-depth guidance on implementing Battery Passports on AWS refer to:

• Building a Battery Passport on AWS: Part 1
• Building a Battery Passport on AWS: Part 2

Sustainability Data Fabric to Accelerate Data Management

The scope of applying agentic AI might vary depending on digital maturity, existing technical capabilities, industry focus, and supply network complexity. A successful implementation requires high-quality, well-governed data to unify product information across operational systems, business applications, and third parties. SDF provides a purpose-built, extensible framework that enables quick and accurate sustainability insights. AWS developed a Guidance for Sustainability Data Management on AWS that provides foundational data management functions using an architecture that is open and modular, and provides integration with in-house and AWS Partner solutions.

Figure 2: Value-chain data lifecycle for DPPs

Using SDF as the foundation for DPP solutions enables AI agents to operate on trusted, verifiable data, resulting in compliant sustainability reporting and strategic decision-making—fueling circular economy business models. Figure 2 provides an overview of the value-chain data lifecycle for industrial customers, from collecting data from operational systems and third parties to data enrichment and preparation, consumption in data products, and concluding downstream sharing. SDF provides data governance, auditability, and lineage of data sets as they pass through.

Scaling Agents with Amazon Bedrock AgentCore

Figure 2 shows two areas where agentic systems enhance the DPP data lifecycle:

1. Agentic AI augments data products by embedding agentic capabilities synchronously into applications through a chat interface, allowing users to interact with data and tools in natural language and instruct an agent to perform tasks on their behalf, such as verifying a particular DPP.
2. For ongoing data acquisition and enrichment, agentic AI asynchronously monitors third-party data sets for gaps, expiring information, for example if validity is restricted to a given quarter or calendar year, or new information becoming available—ensuring up-to-date, valid information is available for internal consumption.

To build, deploy, and operate agents securely at scale, AWS launched Amazon Bedrock AgentCore. Amazon Bedrock AgentCore provides a managed orchestration layer for multi-agent systems. It allows developers to define agent roles, govern interactions, and integrate enterprise data sources securely. Amazon Bedrock AgentCore has observability tools so each agent behaves predictably, traceably, and efficiently in high-volume production contexts. Its services are composable and work with any open source framework and any model, so you don’t have to choose between open source flexibility and enterprise-grade security and reliability. For more information, refer to the Amazon Bedrock AgentCore documentation.

Conclusion

DPPs represent a shift towards transparent, circular economies. While meeting regulatory requirements from 2027 will come with technical and organizational challenges, this is also an opportunity for data standardization and harmonization along value chains, providing enterprises with a holistic view into their operations and builds a foundation for analytics and AI to identify areas for optimization. Capabilities for cross-organizational data sharing can be expanded to further areas, such as quality root cause inspection and forecasting of demand and supply.

Today, there is no one-size-fits-all solution for digital product passports, since customers have varying system landscapes, relevant attributes, and requirements to auditability and verification, with regulations and standards still evolving. SDF, combined with means for cross-organizational data sharing and AI agents, provide an advanced set of capabilities that provide governed visibility into supply networks with automated orchestration of related workflows.

To explore how Amazon Bedrock AgentCore and Sustainability Data Fabric can accelerate your DPP implementation, engage your AWS account team or contact an AWS Industry Partner. Visit AWS for Industries to discover customer success stories, and the AWS Solutions Library for vetted solution accelerators.