Agent Trend: Companies Become Queryable Feedback Systems

TL;DR: The core agent trend is not better chat. It is whether a company can connect real data, real actions, and real feedback.

At the 2026 ChuHaiQu meetup in Beijing, one section of @yucheng’s talk stood out to me: what an AI-native company is actually building.

For context, @yucheng’s X is x.com/yucheng. He is founder of @chuhaiqu (AI Solopreneurs Club), co-founder of @LuciusHQ (Context Layer for Org), and builder of http://tutti.so (Influence Monetization Platform).

The deck is here:

https://presentation-20260612-production.up.railway.app

He named three directions: making the company queryable, connecting actions into loops, and adding passive skills.

I think these three ideas explain where agents are really heading.

§Agents Cannot Stay in the Chat Window

Many agent products today package chat capability as automation.

Answering questions is not the same as completing work.

Generating content is not the same as improving a system.

Calling tools is not the same as creating organizational capability.

For agents to enter real companies, three conditions matter.

First, can the agent read real context?

Second, can it trigger real actions?

Third, can it learn from results?

This is why the three ideas from @yucheng’s talk matter: queryability, loops, and passive skills.

§A Queryable Company Is Layer One

Queryability is not the same as dumping documents into a knowledge base.

A truly queryable company needs customer chats, support feedback, databases, ad data, SEO data, code repositories, and monitoring logs in one context layer.

In the old pattern, companies synchronize information through meetings.

Support tells product what users said. Product asks engineering whether something is possible. Engineering checks the database and code. Growth separately checks ads and SEO.

Information degrades at each handoff.

The goal of a queryable company is that people and agents can directly ask:

What needs are users discussing most?

Can the database support this feature?

Which page caused the search traffic drop?

Where are repeated production errors concentrated?

Without this context layer, even a smart agent can only give surface-level answers.

§Action Loops Are Layer Two

Action loops solve the problem after generation.

Many AI applications stop at one output: a piece of copy, a code snippet, a summary, or a plan.

A real company needs feedback after the output.

Was the copy used?

Was the code deployed?

Did it cause errors?

Did users appeal or complain?

Did conversion change?

Did failed cases flow back?

@yucheng used an automated review example. AI review will make mistakes. When user appeals flow back and prompts are iterated regularly, false positives and false negatives can shrink.

This example is important. An AI-native company should not only chase a good first generation. It should make failures become material for the next, better judgment.

§Passive Skills Are Layer Three

Passive skills were one of the most distinctive ideas in the talk.

Active skills depend on a person initiating the action: open a tool, type a prompt, get a result.

Passive skills keep running in the background.

They may continuously read logs and open PRs when repeated errors appear.

They may continuously analyze support conversations and surface frequent complaints.

They may monitor traffic and flag pages that need fixes.

They may summarize meetings, distribute notes, and track tasks.

These abilities raise the company’s baseline over time. Each passive skill removes one thing that people used to monitor manually.

This is how agents move from assistants to organizational capability.

§Trend Judgment

My view on agents is clearer after this talk.

Some chat-only agents will disappear.

Some fake automation systems without data, permission, or feedback will disappear too.

The agents that remain will be embedded in concrete business systems.

They will read context, execute actions, accept feedback, and keep running over time.

This means companies will increasingly look like queryable feedback systems.

People define goals, judge risks, and handle complex tradeoffs.

Agents query information, execute actions, track feedback, and accumulate passive skills.

§What This Means for Organizations

Middle layers inside organizations will continue to be compressed.

Reporting, relaying, chasing processes, and compiling weekly updates will partly be absorbed by context systems and agent workflows.

The important management capabilities become:

Defining goals.

Designing loops.

Making judgments in complex conflicts.

This is the other side of removing titles.

When the company becomes queryable, handoff roles lose value.

When actions form loops, review no longer depends only on meetings.

When passive skills accumulate, the company does not need a human watching every process manually.

§Closing

@yucheng’s talk was not about how to use AI tools.

It was about the organizational logic of AI-native companies.

The core agent trend is not that models become smarter. It is that companies become better at connecting models to real data, real actions, and real feedback.

Whoever connects these three things is truly entering the AI-native phase.