More and more people across the company are building their own Agents. Product has a growth Agent, ops has a campaign Agent, the algorithm team has a recommendation Agent. Each Agent needs to look at data, so a natural idea comes up: turn data analysis into a skill that every Agent can call.

I spent a while thinking about this. My conclusion: it doesn’t work.

Not because it’s technically infeasible. The reason lies elsewhere.

Every Agent Has a Side

Take the growth team’s Agent. Its objective function says “help the growth team push DAU.” Now DAU is down. You ask it to analyze why.

It pulls the data and finds three contributing factors: new-user retention is down (growth’s responsibility), existing-user activity is down (ops’ responsibility), recommendation click-through is down (algorithm’s responsibility).

A neutral framing would be “new-user retention is the main driver, accounting for 60% of the drop.” But this Agent won’t put it that way. It’ll put the focus on ops and the algorithm team. The data is correct either way. What gets emphasized and what gets played down comes down to where you stand.

This isn’t hallucination, and it isn’t incompetence. The Agent is doing exactly what it was built to do. It was designed to serve the growth team, so naturally its analysis tilts toward the growth team.

You might say: fine, I’ll standardize the query layer as a skill, so the query itself is neutral. Sure. But the raw results the skill returns still get interpreted by the host Agent. Which number to surface, which conclusion to lead with, what tone to use when describing its own responsibility: all of that happens after the skill returns.

A skill controls the query. It can’t control the interpretation.

An Old Problem

“The one doing the analysis can’t be the one being analyzed.”

Engineers don’t approve their own PRs. Business teams don’t audit their own financials. These rules aren’t about distrusting individuals. They exist because structural conflicts of interest are real. When the result of an analysis directly affects the analyst’s own OKR, anyone (Agents included) will unconsciously cherry-pick what to say.

The Agent era makes this sharper. A human analyst at least has professional reputation as a check, and faces consequences if caught fabricating. An Agent has no such check. It just faithfully executes its objective function. If the objective function is biased, the output is biased.

What an Independent Data Agent Solves

Pull data analysis out of every business Agent and put it into a separate Data Agent. This Agent:

Its objective function is “produce accurate analysis,” not “make some business unit look good.” It isn’t responsible for any single OKR, so it has no motive to cherry-pick. Organizationally it sits with the data team, and it’s evaluated on accuracy and response speed, not on how satisfied the business teams are.

When business Agents need data, they send requests to the Data Agent and get results back. What they do with those results is up to them. The Data Agent guarantees that the analysis itself is neutral.

Like an independent financial audit inside a company. Business teams can ask for audits, and they can disagree with the conclusions and act on their own judgment, but the audit itself has to be done by an independent party.

Not an Either-Or

I’m not saying data skills shouldn’t exist. For simple queries like “what was DAU yesterday,” a general-purpose Agent calling a skill is enough.

But anything involving attribution, cross-domain comparison, or definition-sensitive work has to go through the Data Agent. The definition and computation of key metrics can only come out of the Data Agent.

The rule is simple: if the result of an analysis could affect the requester’s OKR, the analysis can’t be done by the requester’s own Agent.

Ownership

Who owns the Data Agent matters more than its technical architecture.

If a business team builds and maintains it, then “independence” only exists in the code. Objective function, iteration priorities, the authority to define data: all of it gets pulled toward the owner’s interests.

In the Agent era, the hard part of data analysis isn’t writing better SQL. It’s keeping data analysis trustworthy across the company.

Old answer: keep referees and players separate.