The guide covers the classic ground well. A cryptographic identity for each agent, credentials that expire in minutes, deny by default, a sandbox around anything that reads untrusted content. If you are standing up agents, hand it to your team.
What a broad framework cannot do is design your system's boundary for you. I keep a working model for agent memory and content trust, and I run new frameworks through it. Three of the gaps are specific to an agent that reads untrusted content and then produces output that can act.
Trust has to travel with the content as a label, not sit at the boundary as a filter. A summary of an untrusted source is still untrusted, so it should not drive an action that reaches trusted internal data.
Review the whole plan the agent means to run, not the single call. Safe tools chain into unsafe sequences, and one call at a time misses the harm.
Output is a set of exits. Each one, including the screen a client renders, needs its own gate, apart from any judgment about the input.
A strong framework stops exactly where your system's design begins. If you were hardening a Zero Trust setup for agents, what would you add?
Written by Duane Grey
AI Strategy & Implementation
Independent AI consultant helping companies cut through hype and deploy systems that produce real results.