---
title: "Taste Agent"
date: 2026-08-06
type: project
blurb: "An experiment in holding my judgment in one place that other AI agents consult, built on a record of my calls that outlives any model."
canonical: https://dazuck.com/projects#taste-agent
---

This project is testing out a light non-consensus thesis I have: that in a multi-agent future, we'll all want an agent dedicated 100% to understanding 'us' and injecting that into every workflow with other agents.

Other agents will specialize in tasks, verticals, projects, types of work, etc. based on their harness and training. And they'll have access to our preferences, memory, data, etc. in many cases. But the thing that differentiates 'me' from anyone else, more than anything else, isn't actually captured well in those discrete files -- it's the taste, judgement, patterns and nuanced day to day things that can only be inferred from a large amount of experience and is best captured in weights, not in files.

This project experiments on that thesis that **taste should live in one deep node that other agents consult**, the way you'd ask the colleague whose judgment you trust rather than hand everyone a style guide. It started as a note to myself: "make a model that is purely embodying my taste - find right tech or way to encode this (maybe not text) and store it. Use as a way to learn a level deeper. And make a partner everywhere."

I didn't start with a model right off the bat. For now it's a thin agent storing a record of my judgments: what I approved, what I changed, what I rejected, and why. It manages, updates, prunes and accesses that record and interfaces with other agents in my workflows to see if having that dedicated expertise improves outcomes and alignment to 'my taste.'

The first experiment captured judgment moments from my own sessions with coding agents and used a small local model to sort out the ones that mattered. The lesson was about order: capture with nothing using it stalled, so I flipped it and started serving judgment first. Now, before an agent shows me a draft or a plan, it asks the taste node, which critiques the work against rules I've written in my own words and cites the rule it's applying. When I override it, that becomes the next example to learn from. It also has a removal rule built in: if it goes unused for two weeks, or I overrule it every time, the agents stop asking it.

In time, if this succeeds, I'll move to a LoRA adaptor or even post-training my own open weight model to really encode this more deeply. For now, it's active and changing week to week. The open question is whether a few hundred of one person's judgments can teach a model something a general one misses.

---
*This is the source markdown for https://dazuck.com/projects#taste-agent.*  
*Full writing + projects index: https://dazuck.com/llms-full.txt*