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ARANEUX

AI services

AI agent development

Agents that read what comes in, decide what to do and do it, with a person in the loop where it matters.

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In short

AI agent development means building software that reads incoming work, decides the next step and carries it out through your existing tools. We build agents for support triage, lead research, document intake and follow-ups. A first live agent for one workflow usually takes three to six weeks and costs $5k to $15k.

Agents that do the boring part of the job, reliably.

An AI agent is software that reads a situation, picks a tool and uses it. The model is the easy part. The hard part is choosing which steps to hand over, giving the agent tools that can't do damage, and knowing when it got something wrong before your customer does.

We start with one workflow your team repeats every day: support triage, lead research, document intake, invoice matching, follow-ups. We automate it end to end, measure it against how it's done today, and widen the scope only once the agent has earned trust.

We've shipped agents on Claude and OpenAI models and test Gemini and open-weight models per task. We build them with LangGraph, the OpenAI Agents SDK, the Claude Agent SDK or plain code when a framework would only get in the way. Every action is a typed tool with permissions, every decision is logged, and every release is tested against real cases from your business.

Is this for you?

You'll get the most out of this if

  • Your team copies information between tools all day
  • You tried a chatbot and it answered questions but didn't actually do anything
  • You have an agent demo that works in a notebook and falls over on real data
  • You need audit logs of what the AI did and why
  • You want to automate a workflow without replacing the people who own it

Deliverables

What you walk away with

  1. 01

    A working agent, live

    Running on your infrastructure or a managed cloud, connected to your real systems through APIs, databases, inboxes or MCP servers. The real ones, with real data.

  2. 02

    Typed tools with limits

    Every action the agent can take is a function with checked inputs and server-side permissions. It can't refund an order or email a customer unless that tool exists and allows it.

  3. 03

    Human review where it counts

    Approval steps for anything expensive, irreversible or customer-facing, so your team stays in control while trust builds. We remove them one by one when the logs say it's safe.

  4. 04

    Logs, evals and a dashboard

    A record of every decision, plus an eval set of real cases we re-run before each change, so improvements never quietly break what already worked.

Timeline

How the project runs

Typical timings for a first live version. We confirm yours in writing after discovery.

  1. Week 1

    Discovery

    We sit with the people who do the work today, pick one workflow, collect real examples and agree on what success looks like in numbers.

  2. Weeks 2 to 4

    First version

    A narrow agent that handles the common cases, running alongside your team with every action reviewed.

  3. Weeks 5 to 8

    Hand over the wheel

    Review steps come out where the data shows it's safe. Edge cases get their own tools or a clean route to a person.

  4. Ongoing

    Run and extend

    Monitoring, model upgrades and new workflows on a monthly retainer, if you want it.

Usual stackClaude, OpenAI, Gemini, LangGraph, OpenAI Agents SDK, Claude Agent SDK, MCP, Tool calling, Python, TypeScript, PostgreSQL, Temporal, Langfuse

Why a small studio

The person who scopes your project is the one who builds it, and the one who picks up when it breaks at 2am.

The founder is on your project
Hussain leads every build himself. You won't brief an account manager who briefs a developer who briefs someone else.
Three people, the output of ten
We build with Claude, Codex and our own agent tooling every day. A small senior team now covers what used to need a department.
We stay after launch
Evals before every release, a log of each AI decision, cost limits and someone on call. Month six matters more to us than demo day.
Your repo, your cloud
The code sits in your GitHub and runs in your AWS, GCP or Azure account from the first commit, on tools your next hire already knows.
Projects shipped
115
since 2014
Clients served
53
US, UK, EU, Middle East, APAC
Average rating
5.0
113 of 113 rated jobs
Years shipping software
18
the founder, since 2008

Case studies

Where we've done this before

FAQ

Questions people ask

What is an AI agent, in plain terms?

Software that uses a language model to decide which step to take next, then takes it by calling a tool: look up a customer, draft a reply, update a record, schedule a call. A chatbot answers questions. An agent goes and does the thing.

Which model do you use?

Whichever does the job at the right cost. We've shipped on Claude and OpenAI, and test Gemini and open-weight models per task. The code is written so the model can be swapped without a rewrite.

Will it work with our existing tools?

Usually, yes. If a system has an API, a database, a reliable inbox or even a web UI, an agent can work with it. We check every integration in week one, before you commit to a build.

What happens when the agent gets it wrong?

It will, sometimes. That's why every action is logged, risky actions need approval, and we keep a set of past mistakes that every new version has to pass before release.

How much does an AI agent cost to build?

A live agent for one workflow usually costs $5k to $15k. Multi-system agents with several integrations and an eval suite run $15k to $50k. Running costs are estimated per thousand tasks during discovery.

Keep exploring

Start here

Let's scope yours.

Send a few lines about the workflow or product. You'll hear back from Hussain within a working day with questions, a rough shape and a ballpark.

Book a free callhello@araneux.com