AI Agents and LLM Systems
Research-grounded analysis of AI agents and LLM systems: architecture, security, memory, RAG, MCP, prompt engineering, model behavior, and evaluation.
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Insecure Inter-Agent Communication: OWASP ASI07 ExplainedHow forged, replayed, or misinterpreted inter-agent messages can trigger unauthorized actions, with controls at mes...
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Cognitive Closure in AI Use: Why We May Stop Too SoonHow the need for cognitive closure may interrupt the evaluation, feedback, and revision needed to improve an AI-gen...
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How to Write for AI Models: The Language of Effective PromptsLearn how to replace conversational filler, theatrical personas, leading premises, and competing tasks with prompt ...
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Role-Play Jailbreaks: A Testable False-Premise MechanismA research-grounded analysis of how role-play may introduce a false safety premise that changes an LLM's interpreta...
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Can AI Lie? The Difference Between Error, Hallucination, and DeceptionA false AI answer is not automatically a lie. This article separates human lying, model confabulation, unsupported ...
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LLM vs. AI System Capabilities: What Architecture AddsA technical map of 26 capability areas: the base-model limitation, the system component that addresses it, the resu...