AI Agents in Production 
Lessons from 10 Enterprise Rollouts 

Almost everyone has an AI agent demo. Far fewer have one in production carrying real work. The gap between the two is not model quality — frontier models are more than capable. It is everything around the model: evaluation, scope, guardrails, and the operational discipline to run a non-deterministic system safely. These are the lessons that consistently separate the rollouts that ship from the ones that loop forever in 'almost ready.'

1. Narrow scope beats broad ambition, every time

The agents that reach production do one job well — triage a support ticket, reconcile an invoice, draft a first-pass response. The ones that stall try to be a do-anything assistant. A narrow agent has a measurable success criterion, a small surface to test, and a clear fallback. 'Handle customer emails' fails; 'classify and route inbound emails into these eight categories, escalate anything below 90% confidence' ships.

2. Build the evaluation harness before the agent

You cannot improve what you can't measure, and you can't safely deploy a non-deterministic system you can't score. The teams that succeed build an eval suite first — a representative set of inputs with known-good outcomes — and run every prompt or model change against it. Without this, every change is a vibe-check and every deploy is a gamble. The eval harness is the single highest-leverage artifact in an agent project, and it's the one most teams skip.

3. Guardrails are not optional

• Bound the action space: an agent that can take destructive actions (send money, delete records, email customers) needs hard limits, not just a good prompt.
• Validate every tool output before the agent acts on it — models hallucinate tool results and arguments.
• Cap iterations and cost per task so a confused agent can't loop into a five-figure bill.
• Require human approval for high-stakes or low-confidence actions — confidence thresholds that route to a person are a feature, not a failure.

4. Keep a human in the loop where it counts

The successful pattern in 2026 is rarely full autonomy; it's the agent doing 80% of the work and a human approving or correcting the consequential 20%. This isn't a transitional crutch — it's the design. It builds trust, produces labelled data to improve the system, and contains the blast radius of mistakes. Agents that quietly take irreversible actions with no oversight are the ones that produce the incident that gets the whole program shut down.

5. Observability and cost control from day one

A production agent needs full traces of its reasoning, tool calls, and decisions — when it does something wrong, you must be able to see why. You also need per-task cost and latency tracking, because agent costs are non-linear and a small prompt change can double token usage. The teams that get burned are the ones who discover both the bad behaviour and the bill after the fact.

6. Most 'agent' problems don't need an agent

The most valuable lesson from ten rollouts: the highest-ROI 'agent' projects often shipped as a deterministic pipeline with one or two LLM calls inside it — not an autonomous loop. Reserve true agentic behaviour for tasks that genuinely require dynamic, branching, multi-step tool use. For everything else, scripted control flow is cheaper, faster, more reliable, and infinitely easier to debug. Starting with the simplest thing that works is the discipline that gets systems to production.

How Infiniti Tech Partners ships agents

We build the eval harness first, scope tightly, wrap every consequential action in guardrails and human approval, and instrument cost and traces from the start — and we'll tell you when the right answer is a pipeline, not an agent. You get a system your team can run and trust, with the evaluation suite and operational runbooks handed over. If you have an agent stuck in demo and want it to actually reach production, start a conversation.