Research Agents

Research agents live or die on how they handle messy, dynamic web data in iterative loops.

Agentic search adapts through multiple retrieval rounds while RAG answers once.

Live retrieval and structured outputs cut hallucinations roughly in half.

Grounding agents in live data, not model weights, cuts hallucination.

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.

Credibility scoring keeps research agents from confidently citing garbage as fact.

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.

Complex research questions demand iterative retrieval loops, not one-shot lookups.

Hybrid retrieval outperforms either semantic or vector search alone by combining their strengths.

Research agents dig deeper and cost more, but they learn as they go.

How agentic design patterns behave differently when tools call live web data.

Different memory types need different retrieval strategies, not a single vector search.

Iterative loops define how deep research agents reliably find and verify complex answers.