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Notes on reliable AI.

LLM reliability, hallucination mitigation, agentic system design, and what actually works when AI meets regulated industries.

August 3, 2026·1 min read

Why hallucination mitigation matters more than model size

Bigger models hallucinate differently, not less. The real gap is between systems that verify their output and systems that don't.

LLM ReliabilityHallucination MitigationModel SelectionRead post

July 29, 2026·2 min read

Orchestration vs. emergence: when multi-agent systems fail

Both orchestrated and emergent multi-agent architectures fail in production. Here's when to use each and what matters for making them trustworthy.

Agentic SystemsMulti-Agent ArchitectureSystem DesignRead post

July 22, 2026·1 min read

RAG systems still hallucinate. Here's what stops it.

Most people think better retrieval fixes hallucination. It doesn't. The fix is bounded generation, confidence thresholds, and explicit fallback chains.

RAGHallucination MitigationLLM ReliabilityRead post

July 15, 2026·3 min read

Prompt Risk Density: measuring hallucination risk early

Most hallucination research fixes the model. My bachelor's thesis, Echo, measures how much risk is already in the prompt, before generation starts.

LLM ReliabilityHallucination MitigationRead post

July 15, 2026·3 min read

Consensus without an orchestrator

Multi-agent systems put a coordinator in charge, which becomes the bottleneck. Mycellium works differently: an auction, a blackboard, a consensus rule.

Agentic System DesignMulti-Agent SystemsRead post

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