This is a follow-up to what I learned running local models in my agent pipeline. That post covered context sizing and KV cache memory. This one covers what I got wrong about concurrency.

The Problem: Agents Queuing Up

My pipeline runs up to four agents simultaneously, called step1-2, step3, step4 …

Most security patch auditing tools look for known vulnerability patterns. They diff a commit, grep for dangerous functions, maybe flag things that look like what last year's CVEs looked like. That works for the obvious stuff. It doesn't work for the commit that says "no behavior change" and silently fixes …

This is the schema spec referenced in How I Audit Security Patches with an AI Pipeline. It defines the SOUNDNESS_CLAIMS format, the empirical proof requirement, and the adjacent-unpatched rule.

SOUNDNESS_CLAIMS Format

Each claim is a falsifiable hypothesis about a specific attack vector. Required fields:

F(x):          The trigger - exact JS …

This is a follow-up to my previous post on routing agents through LiteLLM. That post covered the architecture. This one covers what broke when I actually ran it.

Claude Code Doesn't Pass Through Arbitrary Model Names

The first thing I got wrong: I assumed model: local-sonnet in agent frontmatter would …

This is a follow-up to my previous post where I covered reducing token costs in a multi-agent pipeline. That post touched on local model fallback at a high level. This one goes deeper on how the routing layer actually works.

The Pipeline

I have five agents, split across two tiers …