The introduction of the Adjudicated Query pattern inside Amazon Quick adds a conversational front‑end to a deterministic rules engine for lease‑compliance workloads. Engineers can now let business users type natural‑language questions while the back‑end guarantees that every lease record is evaluated, a requirement that traditional RAG or text‑to‑SQL approaches cannot meet.
Adjudicated Query Pattern Overview
The pattern creates a thin layer that translates user intent into calls on a fixed set of typed operations. The large language model never generates SQL or modifies the population; it only selects the appropriate operation and formats the engine’s response. Rules are stored as versioned data rows rather than code, exposing generic comparison operators such as gte, lte, equals, and exists. Each sweep produces a completeness receipt that asserts the sum of compliant, in‑breach, ambiguous, and unreadable records equals the total scanned, ensuring no lease is silently omitted.
Reference Architecture on AWS
Practitioners wire the flow through several managed services. A compliance officer authenticates via Amazon Cognito, which issues an OAuth token. The Amazon Quick chat agent forwards the token to an Amazon API Gateway HTTP API; the gateway validates the token before invoking an AWS Lambda function. The Lambda hosts the Model Context Protocol (MCP) server and the rules engine, persisting rule versions and sweep results in Amazon Aurora Serverless v2 using the RDS Data API. For exploratory clause‑search, the Lambda calls Amazon Bedrock, but all definitive adjudication stays inside the rules engine. The same data store backs an Amazon QuickSight dashboard, allowing drill‑down on the full result set without involving the LLM.
Operational and Security Implications
Because the LLM never touches the deterministic decision path, the architecture isolates generative AI risk from compliance outcomes. However, the OAuth token flow and API Gateway validation become the primary authentication boundary; any compromise there could allow unauthorized query execution. Auditing must capture the token, the MCP request, the rule version used, and the receipt generated to satisfy defensibility requirements. Scaling considerations include Aurora Serverless v2’s ability to handle tens of thousands of rows and Lambda’s concurrency limits for bursty chat traffic. Monitoring should track receipt completeness failures, which indicate that a sweep could not account for the full lease population.
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What This Means For Practitioners
Adopt the Adjudicated Query pattern when you need provable, full‑population compliance checks that remain accessible via chat. Implement strict token validation at the API Gateway and log every MCP transaction together with rule version identifiers. Use the completeness receipt as a health check in your CI/CD pipeline to prevent silent data loss. Finally, treat the LLM‑enabled clause search as an optional helper, not a source of authoritative decisions, to keep the compliance surface deterministic.
