llm reliability
QUERY = 'contents'; CATEGORY = 'llm-reliability' // 8 rows
Multi-Layer Defense for LLM Production Systems: From Layer 0 to Human-in-the-Loop
Effective defense against LLM failure modes cannot be single-layered. Mode-by-mode matrix and implementation patterns for prompt, output validation, agent guardrails, observability, and human-in-the-loop layers.
LLM Agentic Failure Modes: Task Drift, Reward Hacking, Alignment Faking and More
Eight LLM failure modes that surface only in agent and tool-use systems. Mechanism and defense for task drift, incorrect tool invocation, reward hacking, positional bias, mode collapse, degeneration loops, alignment faking, and version drift.
LLM Foundational Failure Modes: Hallucination, Sycophancy, Context Rot, Instruction Attenuation
Four LLM failure modes that emerge even in a single prompt. Mechanism, academic evidence, and defense patterns for hallucination and confabulation, sycophancy, context rot, and instruction attenuation.
LLM Behavioral Failure Modes: 12 Failure Patterns and the Defense Map
LLMs forget instructions in long sessions, fabricate information, and agree with you against their own answer. A pillar map of 12 systematic failure modes and the defense layers that address them.
AI Agent Protocol Guide: MCP, A2A, UCP, AP2, A2UI, and AG-UI
Six open protocols shaping the AI agent ecosystem in 2026. What each one does, how they relate to each other, and practical use-case scenarios.
An AI agent loop cost someone $187 in 10 minutes. Here's what monitoring tools won't tell you.
AI agents in production have a systemic blind spot: we can trace everything but understand nothing. Current observability tools are flight recorders, not collision avoidance systems.
Domain-Specific Prompt Optimization: The Knowledge Anchor Approach
How do LLMs retrieve knowledge, why do generic prompts fail, and how do knowledge anchors solve this? Structured prompting formats, domain-specific optimization steps, and the practical Prompt Forge approach.