I’ve been noticing something while working with different agent setups.

Most failures don’t come from the model doing something “stupid.”

They come from the system quietly going off track.

Not a crash.
Not an obvious error.

Just slow drift.

You start with something that works:

• agent takes input

• calls a tool

• gets output

• moves to next step

Looks clean.

Then a few things happen:

• slightly wrong context gets pulled in

• tool output isn’t exactly what you expected

• something gets reused that shouldn’t have been

Nothing breaks immediately.

But step by step, things start feeling… off.

That’s the part I think people underestimate.

These systems don’t fail loudly.
They degrade.

And because they still produce output, it’s harder to notice where things went wrong.

A lot of effort goes into:

• better prompts

• better models

• better frameworks

But less into:

• what gets carried forward between steps

• how context is filtered

• what boundaries actually exist in the system

Feels like we’re building systems that can do more…
without really controlling how they behave over time.

Curious how others are seeing this.

When your agents break, is it:

• obvious failure

• or slow drift that’s harder to trace?