Marcus Tewolde

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

Understand what separates LLM monitoring from agent observability.

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

Re-ranking retrieval results puts high-confidence chunks where models actually attend to them.

Decompose RAG answers into claims to catch fabrications that end-to-end scores miss.

Models confidently hallucinate answers about facts beyond their training window.

LLM agents need richer passages than search snippets provide to reason accurately and cite sources.

How to structure webhooks so AI agents don't duplicate work.

Models drowning in tool catalogs need smarter filtering to stay accurate.

Bigger windows won't stop AI agents from forgetting what they learned moments ago.

Fixing hallucination in AI agents requires rethinking retrieval, not just upgrading models.