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Research agents live or die on their retrieval layer, not their orchestration model.

Agentic research systems adapt through multiple retrieval cycles.

Training flaws, not model quality, drive agent hallucinations.

Research agents need live, grounded data during reasoning, not just at training time.

Deciding when AI agents should stop searching the web, not just how to search iteratively.

Memory systems fail not because context windows are small, but because models lose signal in noise.

Research agents need to verify sources before trusting them, not after confidently citing them.

AI agents need ways to verify that what they read online is actually trustworthy.

Structured graph layouts help researchers trace claims to sources and spot contradictions instantly.

Research agents need multi-loop retrieval and reasoning, not one-shot lookups.

Vector and semantic search solve different problems, and hybrid retrieval beats either alone.