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?

In traditional systems, architecture hides in diagrams.

In AI systems, architecture shows up in behavior.

Here s something that surprised us.

AI systems don t just run, they age.

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.

Here s something we don t talk about enough,
AI systems don t always fail, sometimes they just drift.

One day, your model outputs are sharp.
The next, they re slightly off.
Then suddenly, your entire workflow is built on quiet inaccuracies.

Most AI systems assume time doesn t matter.

But in production:
delays compound,
timeouts cascade,
and retries change behavior.

Most code doesn t last.
But the reasoning behind it should.

Good code review captures intent,
why something exists,
what problem it was meant to solve,
and what tradeoffs were accepted at the time.

Meetings.
Incidents.
Messages.
Context switching.

When reviews arrive noisy,
they get rushed.

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

asking questions,
surfacing alternatives,
and making intent explicit.