Production AI breaks in the seams, not the model
Most AI projects don't fail because the model is weak. They fail because retrieval, permissions, evaluation, fallback logic, and product UX get built as five separate workstreams instead of one system that has to hold together under real traffic.
A flashy demo and a system that survives production differ in one place: whether someone drew the boundaries before the first user showed up. That's the thinking behind the AI and Agentic Systems service and the Enterprise AI Assistants with Guardrails project.
Where it actually breaks
Teams bolt on retrieval before deciding which sources are authoritative or how stale data gets refreshed.
Prompts grow until they're effectively policy documents — except nobody can audit a policy document that lives in a string template.
The assistant gets wired into business logic before access control, auditability, or failure handling exist.
Success gets judged on individual prompt outputs instead of whether the workflow saves real time. That gap is exactly what How to Scope an AI Assistant for Real Teams is about.
What a production model actually looks like
Define the job narrowly. One assistant should own one bounded workflow before anyone asks it to feel universal.
Treat retrieval as architecture, not plumbing. Source quality and update cadence matter more than which vector database you picked.
Build explicit fallback paths. A good system knows when to stop, escalate, or ask instead of guessing.
Instrument before you tune prompts further. Logs and qualitative review tell you more than another round of prompt cleverness.
Evaluation has to mirror the actual job
If the assistant supports a support team, evaluate answer quality, safe-escalation rate, and hours saved — not just whether the output looks polished. If it supports an operations team, evaluate latency, failure behavior, and whether a human can trace how it decided something.
The same instinct shows up in From 300M Events to Usable Insight — both domains reward systems you can actually observe and explain, not ones that merely work most of the time.
Related reading
If you're evaluating this kind of work, the useful next stops are services for how engagements are shaped, projects for proof it's been done before, publications for the research side, and open source for how the code actually looks.
The takeaway
Production AI is an architecture decision dressed up as a model decision. Teams that treat it as a governed system ship faster and rebuild fewer fragile demos. If that's the stage you're at, start a conversation.
