Agentic AI in production: patterns that survive real users

Agentic AI — models that plan, call tools and take multi-step actions — has moved from conference slides to real backlogs this year. The demos are dazzling. The production systems are quieter, more bounded, and far more useful.

The gap between the two is almost never the model. It's the engineering around it.

Give the agent less to do, not more

The reliable systems we ship narrow the job. Instead of "an agent that runs the business," we scope a single decision with a clear success signal — triage this ticket, draft this reply, reconcile this invoice. Narrow scope means you can evaluate it, guard it, and roll it back.

A good rule: if you can't write the eval before you build the agent, the scope is still too wide.

Tools are the product

In practice the model is a small part of the surface area. The value is in the tools you expose and how tightly you define them.

Evaluation beats intuition

We build an offline eval set from real examples before launch, and we keep scoring against it as prompts and models change. It's the only way to know whether "the new model" actually improved your task or just felt smarter in a handful of chats.

Once live, we monitor for drift, track tool-call error rates, and sample real transcripts weekly. Agentic systems degrade silently; the instrumentation is what turns a 3am incident into a Tuesday code review.

The honest version

Most organisations don't need an autonomous agent. They need a well-scoped assistant that does one valuable thing reliably, with a human where it matters. That's less exciting to demo and far more likely to still be running next year — which is the only metric we care about.