Data Provenance and Lineage Tracking in Web Knowledge Pipelines
Tracking where AI systems source their answers prevents hallucination and satisfies audits.
Devin Matsukawa
Contributing Editor
Devin Matsukawa
Devin has reported on web infrastructure and open-data ecosystems since the early RSS era, with bylines across developer-focused outlets before joining this publication. He focuses on how live web knowledge flows into AI systems, including crawling ethics, freshness tradeoffs, and the evolving landscape of web retrieval tooling.
6 stories
Evaluating Web-Augmented Agent Performance on Time-Sensitive Tasks
Current benchmarks hide whether failures stem from reasoning, planning, or outdated web content.
Devin Matsukawa
Rate Limiting and Cost Management for Agent Web Search at Scale
One query fans out into dozens of API calls, turning cost control into a core engineering challenge.
Devin Matsukawa
Multi-Agent Architectures That Share a Web Retrieval Layer
Shared retrieval layer quality sets the performance ceiling for every agent that depends on it.
Devin Matsukawa
Embedding Model Selection for Web-Grounded RAG Pipelines
Embedding model choice matters more than benchmark scores for web-grounded RAG.
Devin Matsukawa
Multi-Stage RAG Pipelines for Production AI Systems
Retrieval breakdowns, not model failures, cause most production RAG systems to fail silently.
Devin Matsukawa