Jailen Rafferty

Selectors work best for stable sources; LLMs justify their cost across varied layouts and domains.

Schema-level constraints eliminate the parsing failures that plague production pipelines.

Schema-driven JSON extraction turns messy documentation into clean, structured data for AI agents.

Build research agents with explicit state tracking to prevent hallucination across multi-step tasks.

Attackers hide instructions in web content to hijack AI agents with real-world access.

Agents multiply API costs and quota pressure in ways traditional rate limiting wasn't built for.

Reliable web tools, not better models, make AI agents work in production.

Graph-based retrieval handles multi-hop questions vector search alone cannot answer correctly.

RAGAS separates retrieval failures from generation failures without labeled answer keys.

Separate indexes enforce isolation at the storage layer, not the prompt.

Decide staleness tolerance per topic, not per pipeline.