llm-safety

Covers the practical work of keeping large language models and the agents built on them from causing harm. Topics include failure modes and false assumptions that break agentic systems at scale, prompt injection and jailbreak defenses, guardrails, alignment, evaluation, and red-teaming. Expect concrete design patterns, threat models, and post-incident analysis aimed at engineers who ship LLM features and need them to behave reliably under adversarial and edge conditions.

Balance scale comparing one large AI model against many small routed models in an agentic AI system

agentic-ai, ai-system-design, llm-safety, prompt-injection, ai-architecture, model-routing

Six Fallacies That Break Agentic AI Systems (And How to Design Around Them)

Six false assumptions break most agentic AI systems before they scale. This post names each fallacy, documents the real-world damage it causes, and outlines the design patterns that prevent it.