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

1. What I’m Researching in AI Right Now — the research map and trust through-line
2. AI Evals Are Broken — why public benchmarks stopped measuring real capability
3. Securing AI Agents — MCP hardening, confused deputy, and what I run on my home stack
4. World Models: What Comes After the Language-Only Era — when text-only agents hit their ceiling
5. 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