Maya Okafor

Deep research agents iterate through retrieval and reasoning loops to synthesize answers.

Detect which pages need browser rendering before you pay the cost of running them.

Page retrieval—not extraction—is where most web scraping fails for AI agents.

Crawlers that ignore robots.txt are multiplying fast and harder to stop.

Web data access, not framework architecture, determines whether research agents work at scale.

Four types of hallucination require different fixes matched to their root causes.

Raw web content and stale information degrade AI agent performance far more than token limits do.

Visualizing knowledge graphs turns research agent outputs into navigable, fact-checked networks.

Hybrid retrieval fixes production failures that pure vector search creates silently and at scale.

Four layers and patterns determine whether web agents research reliably or fail predictably.

Forward and reverse proxies solve opposite problems, and confusing them breaks crawling systems.