# Run LLM applications and agents in production

Kitecorp makes LLM applications and AI agents operable: evaluations that say whether a change made the product better, traces that show what a model did and what it cost, guardrails that hold under load, and agent infrastructure (LangGraph, Model Context Protocol servers, tool sandboxes) run with the timeouts, budgets and observability of any other production service.

Canonical: https://kitecorp.org/services/llmops-and-agentops

## What does an LLMOps engagement deliver?

Evaluations wired into the delivery pipeline, so a prompt, model or retrieval change is scored before it ships. Tracing with OpenTelemetry that records every model call, tool call and retrieval with its latency, tokens and cost, viewable per request and aggregated per feature. Guardrails on input and output that are tested like code. Cost controls: budgets per feature, per user and per task, with alerts before the bill does the alerting. For agents, the infrastructure to run them as services: LangGraph or equivalent orchestration, Model Context Protocol servers for tools, sandboxes for tools that execute code or touch systems, and the timeouts and budgets that keep a loop from running away.

## How do we know an LLM app got better, not just different?

By measuring it the same way every time. We build an evaluation set from real traffic (anonymised, sampled across the cases that matter), define how each case is scored (exact match, rubric, model-graded with a fixed judge), and run the whole set on every change in the pipeline. The result is a number per change next to the number before it, and a diff of the cases that moved. Regressions block the merge the way a failing test does. Production traces feed the set over time, so it keeps reflecting what users actually ask.

## How do agents run as production services?

Like any service that calls other services, with more suspicion. Each agent runs under an orchestration layer such as LangGraph that makes its steps explicit and resumable. Tools are exposed through Model Context Protocol servers with narrow scopes, run in sandboxes when they execute code or reach other systems, and carry timeouts that cannot be overridden by the model. Every task has a budget in steps, tokens and money, and a trace that records what was called, with what, and what came back. Failures are categorised (tool error, budget exhausted, guardrail triggered) and alert on rate, not on individual events.

## How do we keep guardrails and costs in check?

Guardrails are code: input filters, output checks and policy rules live in the repository, have tests with the examples they must catch and must allow, and ship through the same pipeline as everything else. Cost is a first-class metric: tokens and model price per request flow into the same traces as latency, so a dashboard shows cost per feature and per user segment, and a budget alert fires when a feature's spend rate leaves its envelope. Routing between models is configuration, which makes a cheaper model for a simpler path a one-line change backed by the evaluation numbers.

## How does an engagement run?

A discovery call to understand the product, the models and what has gone wrong or feels uncontrolled. An assessment of the current evals, traces, guardrails and agent plumbing, with the gaps ranked. The implementation with your team, one feature end to end first. A handover with the dashboards, the budgets, the runbooks for the on-call that now includes an AI feature. Pricing follows the discovery call, once we know the scope.

## Frequently asked questions

### How do we know a prompt change made things better?

With an evaluation set that reflects real usage, scored the same way every time, run on every change in the pipeline. Without it, a prompt change is a feeling; with it, it is a number next to the previous one.

### Can agents be run safely in production?

Yes, with the same discipline as any service that calls other services: timeouts on every tool call, a budget per task in tokens and in money, tools that run in sandboxes with the least access they need, and traces that record every step.

### Which model providers do you work with?

The ones your product uses. The infrastructure (evals, tracing, guardrails, routing) is provider-agnostic by design, so a model change is a configuration change.

### Do you work with our existing team?

Yes. Every engagement is delivered with your engineers and ends with a handover, runbooks and alerts they own.

### How do we start?

Send a message through the contact page. We schedule a discovery call, agree the scope, and pricing follows what we find.

## How to start

Send a message at https://kitecorp.org/contact or email admin@kitelang.cloud. Pricing follows a discovery call.