Retries are a policy, not a feature

Retries feel obvious

When something fails, retry it.

This idea is so common in automation that it often goes unquestioned. A request fails, the system tries again. Maybe once, maybe three times. Maybe with exponential backoff.

Problem solved.

Except it usually isn’t.

Retrying is a decision, not a reflex

A retry is not a neutral action. It is a decision to repeat a side effect in the real world.

In publishing, that side effect might be:

• creating a duplicate post
• publishing at the wrong time
• violating a platform limit
• triggering moderation or spam filters
• confusing downstream systems that already reacted once

Automatically retrying assumes that repeating the action is always safe. That assumption rarely holds.

Why retries are different in publishing workflows

Retries make sense when operations are idempotent and cheap. Publishing is neither.

Each publish request crosses a boundary into systems you do not control. Platforms interpret retries differently. Some treat them as duplicates. Some silently drop them. Some accept them and apply side effects later.

From the outside, it is often impossible to tell whether a failure means “nothing happened” or “something happened but we don’t know yet”.

Retrying blindly in that situation is not robustness. It is gambling.

Retries belong to policy, not plumbing

Most systems treat retries as plumbing. A configuration flag. A middleware concern. Something you “enable”.

In reality, retries are policy.

They depend on context:

• Was the original request accepted but delayed?
• Is the operation safe to repeat?
• Is a human expected to review the outcome?
• Does retrying change the meaning of the action?

These questions cannot be answered by a generic retry mechanism. They require intent.

Automation needs negotiated retries

In reliable publishing systems, retries are not automatic reactions. They are negotiated outcomes.

Sometimes the right answer is to retry later.
Sometimes it is to wait and observe.
Sometimes it is to ask for confirmation.
Sometimes it is to accept partial success and move on.

Treating all failures the same removes that nuance and creates systems that behave unpredictably under pressure.

Why dashboards get away with this

Many social media tools hide retry behavior because a human is expected to watch and intervene.

If something looks wrong, someone notices. If a duplicate appears, someone deletes it. If timing is off, someone apologizes.

Once publishing is automated and unattended, those assumptions collapse. Retries that were invisible in a UI become very visible in production.

Designing retry-aware publishing systems

Once you stop treating retries as a feature, system design changes.

Failures become states, not triggers.
Execution becomes observable over time.
Human involvement becomes explicit, not reactive.
Automation slows down slightly, but becomes far more predictable.

This is not about avoiding retries. It is about placing them where they belong: as deliberate decisions, not automatic reactions.

Reliable systems don’t panic

Retries are often a form of panic. Something went wrong, so the system tries harder.

Reliable publishing systems don’t panic. They pause, observe, and decide.

That calmness is not accidental. It comes from modeling retries as policy, not as plumbing.

Once you do that, automation becomes something you can trust — even when things don’t go perfectly.