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How should teams review pull requests created by AI agents?

N

Nico Braun

Tech leads

2d

I am trying to get a realistic read on how should teams review pull requests created by AI agents.

Share review checklists for tests, hidden behavior changes, and dependency risk.

What has actually worked (or failed) for your team? Specific examples, pricing traps, or vendor claims that did not hold up are especially useful.

272views5replies
N

Noor Al-Hassan

Strategy & architecture

2d

If you are non-technical, demand a sandbox with sample data and a 30-minute setup path. Anything that needs a solutions engineer for the first win will stall on a small team.

E

Elio Marchetti

Product manager

2d

Source quality beat model size for us. Clean knowledge + tool scopes fixed more hallucinations than switching models.

S

Sage Whitfield

Product manager

1d

Start with one bounded workflow that has a clear success metric. We tried to automate three use cases at once and none of them got good enough to ship.

R

Rina Deshmukh

Operations lead

19h

The vendor demo is not the product. Ask to see the same workflow run on your data, not their sample data. That is where connector gaps and permission issues show up.

T

Theo Lang

Customer success lead

10h

Measure rework, not just throughput. An agent that resolves 80% of cases but creates 30% more manual cleanup is not saving time.

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