Policy & Practice | Fall 2026

The economics favor a portfolio of incremental investments over high-stakes, all-or-nothing replacements. Shared components and standards become more viable, reducing duplicated effort across programs and states.

risk profile is far more manageable, and agencies see value at each stage rather than waiting years for a single delivery. What AI-Assisted Development Actually Adds Modular modernization has stalled in the past, less because of flawed strategy than because of the economics of execution. AI-assisted development is starting to shift that equation. Generative coding tools, in the hands of skilled developers, can draft inte gration code, generate test cases, and produce documentation in a fraction of the time these tasks used to require. The same tools help experienced teams thoroughly assess legacy systems: surfacing hidden logic, discovering undocumented dependencies, and defining how data truly flow through the system. Identifying these complexi ties early helps shape a modernization road map that delivers incremental value while reducing pressure on state staff. Used well, these tools can raise the bar on what a knowledgeable team can accomplish. The expertise still has to be there, but AI makes it go further. The Modular Rules Engine: Where AI and Formal Verification Converge Of all the components in an eli gibility system, the rules engine is the one most directly responsible for correctness, consistency, and compli ance. A typical mid-size state manages hundreds of interdependent eligibility rules across programs, with federal and state versions that must stay syn chronized. Changes to one program’s criteria frequently cascade across others. Manual processes struggle to trace those dependencies safely, and bugs discovered weeks into production can cause systematic overpayments or wrongful denials affecting thousands of applicants before they are detected. A modular rules engine, one that encodes income calculations, house hold composition logic, categorical eligibility pathways, and other policy rules in discrete, testable, independently updatable modules, is a foundational asset for any modern benefits platform. It does not

determination is backed by a formal proof chain that provides defensible evidence for federal compliance reviews and applicant appeals. For states, this means the rules engine can finally keep pace with the velocity of federal policy change, without the risk of silent errors accumulating in production.

eliminate the interconnectedness of eligibility logic, but it makes that interconnectedness explicit and man ageable. When federal policy changes, the affected rule modules can be iden tified, updated, tested, and deployed without requiring changes to unre lated parts of the system. Accenture and AWS have access to capabilities that take this further than traditional rules engine approaches. AWS Automated Reasoning, available through Amazon Bedrock Guardrails, uses formal mathematical verification to prove that eligibility decisions are correct, not just that rules are executed as coded. When a new federal rule is introduced, the system can instantly verify whether it conflicts with existing state rules across all programs before deployment, rather than discovering contradictions weeks into production. Rule changes that once required two to four weeks of manual specification, coding, and regression testing can be validated and deployed in one to two days, with formal proof of correctness serving as an audit artifact. This matters because eligibility determinations require deterministic, auditable logic, not probabilistic infer ence. Agentic AI is a powerful tool for adjacent tasks: document processing, data ingestion, case quality review, case triage, and derived insights. But the core rules engine demands certainty. The combination of large language models (LLMs) for inter preting regulatory language and Automated Reasoning for formal verification delivers both: AI’s ability to rapidly translate federal guidance into structured rule specifications, backed by mathematical proof that those specifications are logically consistent and cross-program compliant. Every

A Moment Worth Taking Seriously When modular architecture,

AI-assisted development, and formal verification work together, the relation ship between policy and technology starts to shift. Policy teams can see their intent reflected in systems within a realistic time frame. The economics favor a portfolio of incremental invest ments over high-stakes, all-or-nothing replacements. Shared components and standards become more viable, reducing duplicated effort across programs and states. States pursuing this path need partners who bring both technical depth and policy knowledge. AI tools can accelerate the work considerably, but decisions about where and how to apply them depend on domain knowl edge that takes years to develop. None of this is simple, and the expertise required is substantial. For states willing to invest in under standing their architecture before changing it, the tools are now avail able to make modular modernization an achievable idea.

Heidi Reed is the Integrated Eligibility Lead at Accenture.

Lauren Aaronson is the HHS Technology and Innovation Lead at Amazon Web Services (AWS).

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Fall 2026 Policy & Practice