Research Agents

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

Benchmark scores miss the multi-step failures that tank agents in production.

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

Iterative agents outperform single-pass retrieval on complex reasoning tasks.

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

Cascading errors in agent workflows compound at each reasoning step.

Knowing when to stop retrieving determines whether AI agents finish efficiently or spin endlessly.

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

Separate source credibility from overall quality scores to catch citation failures before they ship.

AI agents researching the web need to verify what they find before trusting it.

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

Live web retrieval solves the staleness problem plaguing traditional vector databases.

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

One system stops after a single response, while the other keeps searching until it's satisfied.

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