Research Agents
RAG Pipeline Architecture for Research Agents
Research agents need loops, not one-shot retrieval, to handle ambiguous questions and live data.
Everett Nadkarni
Features Editor · · 11 min read
Research agents need loops, not one-shot retrieval, to handle ambiguous questions and live data.
Research agents dig deeper but cost more; answer engines are fast and cheap.
How agentic design patterns break down when your agent's tools hit live web data.
Prioritized queues, domain affinity, and deduplication keep crawlers fast and polite at scale.
Agents can't see when cached data expires, turning stale information into silent failures at scale.
Detect what actually matters before you scrape, or drown in noise.
Cleaning HTML to Markdown cuts token waste by 80% and measurably improves model accuracy.
A four-stage pipeline transforms messy web content into data LLMs can reliably reason over.