Not because people don t care.
Because attention is limited.

When reviewers are exhausted,
they miss context,
skim changes,
and default to approval.

PRFlow was built to remove the noise before humans step in,
lint-level issues, obvious mistakes, missing basics.

It doesn t replace reviewers.
It protects their focus.
If you want to try it on real PRs, here is the link : https://www.graphbit.ai/prflow

Hey Product Hunt community,

(Picture this)It s 4:30 PM on a Friday. You ve just cracked the logic for that complex auth-flow refactor. You ve been in the zone for six hours straight headphones on, coffee empty, absolute flow state.

You run the tests. Green.

You check the linter. Clean.

Teams aren t just adding AI into existing workflows.
They re reshaping the workflows themselves with the AI agent in the loop.

Steps get removed.
Objectives become clearer.
Old constraints disappear.
Processes reorganize around what the system can now do natively.

The real productivity gain isn t automation.
It s rethinking the architecture entirely.

Curious to hear from this crowd:
What s one workflow you rebuilt because AI made the old version irrelevant?

Some teams comment on performance.
Some on readability.
Some on test coverage.
Some on edge cases.

Over time, those comments shape behavior more than any guideline.

The best reviewers don t act like gatekeepers.
They act like collaborators :

asking questions,
surfacing alternatives,
and making intent explicit.

Wrong abstractions.
Leaky boundaries.
Hidden coupling.

Bugs get fixed.
Decisions linger.

Not through policy.
Through necessity.

If you can t explain it clearly,
you can t delegate it to an agent.

Here s something uncomfortable I ve learned building AI agent systems:

AI rarely fails at the step we re watching.

It fails somewhere quieter
a retry that hides a timeout,
a queue that grows by every hour,
a memory leak that only matters at scale,
a slow drift that looks like variation until it s too late.

Most teams measure accuracy.
Some measure latency.

Even simple commands become a negotiation between :

intent,
interpretation,
context,
and constraints.

Interfaces aren t static ,
they re conversational.

Agents can complete actions reliably,
but accountability for direction and outcomes
still sits with humans.

That creates a clearer map,