Building Agent-Callable Evidence from Government Open Data

说明与用途

CivicDataForge 开发了一套工作流程，将政府开放数据转化为可供 AI 代理使用的证据。该解决方案解决了分页问题、明确的决策语义（例如“无已发布的匹配项”）以及严格的来源追踪，以确保 AI 代理和生产系统的可靠性。

问题

Government open data is often incomplete, paginated, or structurally inconsistent, making it unreliable for production systems or AI agents that require definitive evidence rather than just raw data.

结果

The team established a rigorous framework for treating government data as evidence, ensuring completeness, provenance, and clear semantic boundaries for AI agents, moving beyond simple scraping to reliable evidence delivery.

工作流

• Discover publisher page limits and request every page in a stable order.
• Track page count, observed row count, and source-reported totals.
• Reject monitoring baselines if the query is capped, partial, or structurally inconsistent.
• Bind the completed scope into a receipt with source metadata.
• Use an explicit decision vocabulary (EVIDENCE_FOUND, NO_PUBLISHED_MATCH, etc.) instead of binary yes/no.
• Normalize data while retaining lineage (publisher, endpoint, query, timestamp, original field names).
• Monitor independent dimensions: availability, schema compatibility, record-count behavior, and freshness.
• Implement idempotent webhook handlers using actor_run_id + event_type as keys.
• Define task-to-tool selection contracts for AI agents specifying boundaries and minimum inputs.

证据

A response can be valid JSON, have a 200 status, and still represent only the first page.

来源

We use an explicit, fail-closed decision vocabulary: EVIDENCE_FOUND, NO_PUBLISHED_MATCH, REVIEW_REQUIRED, SOURCE_UNAVAILABLE, SCOPE_INCOMPLETE.

来源

idempotency_key = actor_run_id + ":" + event_type

来源