Real-Time Data Integration

Systems must detect when LLM knowledge grows stale to trigger timely web retrieval.

Typed retrieval calls with latency bounds prevent reasoning drift across agent iterations.

Event-driven ingestion cuts detection latency and aligns freshness with production agent reasoning.

Five controls balance autonomous agent retrieval against shared API capacity limits.

Architectural choices about where AI agents fetch facts matter more than which model processes them.

Stale search indexes make AI systems confidently wrong instead of cautiously uncertain.

Clean web content extraction prevents downstream failures that appear later in the pipeline.

Financial LLMs need live data to answer real-time questions accurately.