---
title: "Building with Claude"
section: "Technologies"
canonical_url: "https://leverge.ai/technologies/claude"
topic: "Claude development services"
published: "2026-06-02"
updated: "2026-08-01"
publisher: "Ailoitte Technologies Private Limited"
---

# Building with Claude

Claude is the model we most often reach for on the reasoning-heavy steps of an agent workflow — multi-step tool use, long-context document work and tasks where instruction adherence matters more than raw speed. It is not the right choice for every step, and routing high-volume classification or extraction through it is one of the most common causes of indefensible inference bills. We benchmark step by step and use it where it measurably wins.

## Key takeaways

- Claude's strength in our production work is multi-step tool use and instruction adherence, which is exactly what agent loops depend on.
- Long-context handling makes it well suited to document-heavy tasks where relevant material is spread across a large input.
- Routing routine classification and extraction through a frontier model is the most common source of unnecessary inference spend.
- Prompt caching on long stable context changes the cost profile substantially and is worth designing the prompt structure around.
- Model choice should be a per-step decision validated against your evaluation set, not a platform commitment made once.

## Where it fits in an architecture

We treat model selection as a per-step decision, not a platform choice. In a typical
agent workflow there are three or four genuinely hard steps — deciding what to do
next, reasoning over a long retrieved context, composing a careful response — and a
dozen routine ones.

Claude goes on the hard steps. Something small and cheap goes on the routine ones.
That split is the single highest-leverage cost decision in an agent build, and it is
much easier to design in than to retrofit once the architecture assumes one model
everywhere.

## What changed our patterns

Two capabilities materially altered how we build.

Prompt caching made long stable system context economically sensible. Before it, a
detailed system prompt with extensive tool definitions and policy context was a
per-request cost you tried to trim. With it, that context can be substantial and
cached, which in practice means agents can be given fuller instructions than we used
to allow ourselves.

Reliable structured tool calling removed a whole layer of defensive code. We still
validate every output against a schema at the boundary — that rule does not change
for any model — but the repair-retry path fires far less often than it used to.

## The mistake we most often inherit

Almost every underperforming system we are asked to audit routes every step through
one frontier model. The bill is fine in pilot and indefensible at volume, and by
then the code assumes a single client everywhere.

The fix is a provider abstraction and per-step benchmarking against the evaluation
set. It is a day or two of work at the start of a project and a multi-week refactor
eighteen months in.

## Frequently asked questions

### When do you choose Claude over another model?

For steps where the model has to follow a multi-part instruction precisely, chain several tool calls without losing track of the goal, or reason over a long document where the relevant material is scattered. Those are the characteristics of the reasoning core of an agent loop, and in our benchmarking Claude is consistently strong on them. We still verify against your evaluation set, because the ranking between frontier models shifts with each release and your data may not behave like our benchmarks.

### Is Claude expensive to run in production?

It is a frontier model priced accordingly, which matters only if you route everything through it. In practice a well-architected system sends the judgement-heavy steps to Claude and the high-volume routine steps — classification, field extraction, formatting — to a much smaller model, which typically cuts total inference spend by four to eight times with no measurable quality change. Prompt caching on long stable context reduces it further.

### Can Claude be used with sensitive data?

Yes, with the right access path. Through AWS Bedrock or Google Vertex inside your own cloud tenancy, requests stay within your region and under agreements you already hold, with zero data retention configured. That is the pattern we default to for healthcare and financial services clients. Direct API access with zero-retention terms is also available where your compliance posture allows it.

### Do you lock the system to one model provider?

No. Every system we build talks to our own interface with model selection in configuration, so switching provider is a config change plus an evaluation run rather than a refactor. Given how often pricing and capability shift, treating any single model as a permanent architectural commitment is a mistake.

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Source: https://leverge.ai/technologies/claude — Leverge