Multi-agent trust infrastructure: Behavioral scoring for local deployments

<!-- SC_OFF --><div class="md"><p>Question for people building agents. The discussion around output safety I understand, but what are you doing for input-side defense?</p> <p>I

<!-- SC_OFF --><div class="md"><p>The idea is simple, if one agent is good then multiple agents working together must be better. In practice coordination between agents is an

<!-- SC_OFF --><div class="md"><p>I've been following the IETF drafts for agent identity, and I realized there's a massive hole: User-to-Operator trust. We have ways for services

<!-- SC_OFF --><div class="md"><p>I've been messing around with local models to see when they fail silently or confidently make stuff up. One test I came up with is a bit wicked

<!-- SC_OFF --><div class="md"><p>I've been thinking about this as local models get better at </p> <p>tool use — shell access, file system, HTTP requests, MCP. </p> <p>If a prompt

<!-- SC_OFF --><div class="md"><p>I've been thinking about why local-first AI agent architectures are getting serious enterprise traction in 2026, beyond the obvious &quot;keep

<!-- SC_OFF --><div class="md"><p>want to put a different multi-agent direction on the table because most of what i see assumes task teams.</p> <p>the dominant pattern right now:

<!-- SC_OFF --><div class="md"><p>Most of my LLM cost was on the wrong tier of work.</p> <p>Classification, extraction, JSON formatting, summarization I'm going to review anyway.