We replaced two agent steps with ordinary application code and the result became faster, cheaper, and easier to debug.
What is included
- Use models for ambiguity, not plumbing
- Keep permissions and money movement deterministic
- Prefer schemas at every boundary
- Escalate uncertainty instead of improvising
Why I am sharing this
The useful part is not only the final implementation. I would like this thread to capture the trade-offs, failure modes, and practical details that help another builder make a better decision.
Clear constraints and reproducible examples make technical discussion dramatically more useful.
Join the discussion
Which part of your workflow genuinely benefits from model reasoning?
Share your environment, constraints, and what you have already tried. Screenshots, traces, small code samples, and counterexamples are welcome.
3 Likes
On When should an AI workflow stay deterministic?:
This matches what we saw in a recent implementation.
For teams trying this, I would start with ten representative fixtures and run them continuously. A small trusted suite is more valuable than a large benchmark nobody reviews.
Practical next step: record one baseline with cost, latency, and failure reason before changing the architecture. That gives the team something concrete to compare.
— Theo
4 Likes
On When should an AI workflow stay deterministic?:
One detail I would add from operating a similar system:
The user-experience side matters too: expose evidence, make uncertainty visible, and always provide a clear path to correct or escalate the result.
Practical next step: record one baseline with cost, latency, and failure reason before changing the architecture. That gives the team something concrete to compare.
— Nia
5 Likes
On When should an AI workflow stay deterministic?:
I tested a smaller version of this pattern last month.
We got the best result after separating retrieval quality, model quality, and application failures into different dashboards. A single success metric made every regression harder to diagnose.
Practical next step: record one baseline with cost, latency, and failure reason before changing the architecture. That gives the team something concrete to compare.
— Tomás
2 Likes
On When should an AI workflow stay deterministic?:
The framing here is useful, especially the focus on measurable behavior.
The first version was clever but difficult to inspect. Moving state into explicit records and logging every boundary made retries safer and incident reviews much faster.
Practical next step: record one baseline with cost, latency, and failure reason before changing the architecture. That gives the team something concrete to compare.
— Sofia
2 Likes