Enterprise · Agents
Enterprise AI Agents That Survive Contact With Reality
Everyone has seen the demo: an agent books, schedules, summarises and charms, all in ninety seconds, all on rails. Then the enterprise tries it on real work — messy data, edge cases, compliance, Monday mornings — and the agent falls over. I build production agent systems at Ishara Solutions for clinics and at AOLOW for logistics operations. This essay is the field manual I wish someone had handed me: what separates agents that survive from agents that demo.
1 · Scope the job, not the magic
The most common failure is scoping an agent around a capability ("an AI receptionist!") instead of a job ("no missed appointment request ever waits more than five minutes for a correct response"). Jobs have boundaries, owners and measurable outcomes. Capabilities have demos. Define the job first, in one sentence, with a number in it.
2 · Evaluate like an auditor, not a fan
If you cannot score it, you cannot ship it.
Every production agent needs an evaluation harness before it needs a nicer prompt: a set of realistic cases, graded outcomes, and regression runs on every change. In clinic scheduling this means synthetic patients with conflicting constraints; in logistics, historical weeks replayed against the agent's decisions. The harness is the product. The agent is just the current best weights-and-prompts passing through it.
3 · Guardrails are architecture
Constraints belong in the system design, not in a prayer appended to the prompt. Least-privilege tool access. Confirmation gates before irreversible actions. PII minimisation by default. Deterministic validation of every structured output. Escalation paths to humans that are fast and dignified — for the customer and the employee. In healthcare contexts especially, the standard is not "usually correct" but "correct, auditable, and safe when wrong."
4 · Infrastructure decides
Agents are only as good as their substrate: identity and permissions, logging and observability, cost controls per task, graceful degradation when models or tools fail. Most agent post-mortems I have read are infrastructure post-mortems wearing agent costumes. Build the boring parts first — they are the parts that fail at 2 a.m.
5 · Start with the workflow, end with the agent
Counterintuitive but reliable: automate the workflow deterministically first, then add intelligence at the decision points. A scheduling pipeline with one smart triage step beats a fully autonomous agent with no spine. Autonomy should be added where judgment pays, and withheld where determinism suffices. The art is knowing which is which — and that knowledge only comes from operating the workflow yourself.
The standard
An enterprise agent earns its place when it is correct more often than the process it replaced, cheaper per task, auditable when questioned, and graceful when uncertain. Four bars, all measurable, none negotiable. Everything else — model choice, framework, prompt craft — is implementation detail.
That is the standard we hold at Ishara Solutions and AOLOW. It is slower than demo-driven development. It is also the only kind that compounds into trust — and trust, in enterprise technology, is the whole business.