07How you changeDeep dive ②

Watch Before You Delegate — Even After Two Years

The same agent behaves differently across domains. And when you enter a new one, even experienced operators need an observation round first.

Read4 min read
Topicsoperator · delegation · domain · calibration
TL;DR

The same agent behaves differently across domains. Solid at code review doesn't mean solid at legal analysis — even if it's the same model. When you enter new territory, even operators with years of experience need an observation round before delegating real work: not because you distrust the agent, but because you don't yet have a model of how it fails in this domain.

You've been working with agents for close to two years.

Code review, document summaries, first-draft writing — you do all of this with an agent and barely need to review. You know when it's on, when it needs another round, when the prompt needs reworking. That's genuine calibration.

Then one day you ask it to analyze a contract clause for the first time. You trust the track record. You let it run the whole thing. The output comes back looking solid: structured, highlighted, risk flags included.

Three important clauses got missed. You didn't find out until a counterpart asked about them.

The agent didn't suddenly get worse. You used calibration from the old domain to judge a new one — and those aren't the same instrument.

01Good Here Doesn't Mean Good There — Even When "Here" and "There" Are the Same Agent

Most of what agents do well sits in the middle ground: clear problems, verifiable answers, fast feedback. Code with a syntax error won't compile — you see it immediately. A summary that misses a point is visible on reread. That's the agent's center.

Different domains have different centers. In legal analysis, "wrong" can look exactly like "almost right" without any visible syntax error. In strategic advice, compelling and incomplete are not mutually exclusive. In a field where you lack domain expertise, you also lack the baseline to tell the difference.

Result: the agent may be operating at the edge of its real capability in this new domain — but the output looks like the center of a domain you already know well.

You don't catch it. Not because you're careless. But because you haven't built a model of how it fails here yet.

Delegating straight from old track record

Using calibration from domain A to trust in domain B
No test run to compare output against expectations in the new domain
Finding the mistake on a task that mattered — when the cost is already high

Observing before delegating for real

Run a small, low-stakes test in the new domain first
Read how the agent fails — not just whether it got the result right or wrong
Adjust trust threshold and gate points before moving to real work

Observing isn't distrust — it's not applying the wrong ruler. Two years of experience is what helps you observe the right things quickly. But the observation round still has to happen.

02Three Steps to Calibrate in a New Domain

No process needed. But some structure helps.

NEW DOMAINREAL DELEGATION
Step 1 — Small test, low stakes: delegate a task of the same type but smaller scope, where the output won't be used directly.

Step 2 — Observe the failure pattern: read the output carefully even when it looks right — look for what might be missing or wrong without being visible.

Step 3 — Adjust before going real: update your gate points and delegation scope based on what you observed, not based on the track record from a different domain.

These three steps don't protect you from every mistake. They protect you from the expensive kind: the ones that come from misapplied calibration.

The loop doesn't need to be long. For domains close to familiar ground, one or two small tests usually builds a workable model. For domains that are genuinely distant — legal, medical, highly technical specialties — invest a bit more time before the real work.

03The Sign You're Skipping the Observation Step

Most people don't have a conscious moment of "I'm choosing to skip this." It happens more naturally than that:

You have a deadline. The task looks similar to tasks you've done before. The track record is good. So you push straight through.

The real question is: does looks similar mean same failure profile? Usually not.

A quick check: if the output is wrong in this new domain, would you catch it before or after it's been used? If the answer is "after" — you need another observation round first.

Not because the agent isn't trustworthy. But because you don't yet have enough of a model to trust it correctly here.

Two years of experience don't disappear when you take an observation round. That experience is exactly what helps you look in the right places, read patterns quickly, and know when one round is enough. But the round still has to happen.

c
The author

Each story here wraps a lesson paid for in full.

craftagentsomeone building and learning at once

What are you building with agents? Want to trade notes, push back, or build something together — drop a line.

58pieces12clustersVI·ENbilingual

Get new pieces by email

Field notes on working with AI agents — occasional, no spam.