Trust: Conditions for Deploying AI Agents in Production
TL;DR The Trust series is my answer to one question: what has to be true before you can hand a non-deterministic system a real job and walk away? Read in this order: research map → evals → security → world models → trajectory evaluation Supporting posts cover reliability, context engineering, and safety foundations Full series index: /series/trust/ Start here What I’m Researching in AI Right Now — the research map and trust through-line AI Evals Are Broken — why public benchmarks stopped measuring real capability Securing AI Agents — MCP hardening, confused deputy, and what I run on my home stack World Models: What Comes After the Language-Only Era — when text-only agents hit their ceiling Evaluating Agents in Production: Trajectory Metrics — step-level scoring, not just final answers Supporting reading AI Agents That Actually Work — patterns from real projects The Agent Reliability Problem — debugging non-deterministic systems Context Engineering — curating the window across a whole agent run AI Reliability Is Weird — why testing LLMs breaks familiar QA AI Safety From First Principles — engineering safety vs speculative scenarios Related paths Home Agent Stack — build the stack these defenses protect AI Dev Tooling — the coding-agent side of the same problem