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Custom AI and software · AI agents

AI agent development company: custom agents built to order

Vexio is an AI agent development company. We build custom AI agents for your organisation: software that works toward one defined goal by planning steps and calling your tools. You set what it may do, and every action it takes is logged. We write agents in Python and Node.js, connect them to your systems with MCP and deploy them on Cloudflare, AWS or Azure. If the agent needs a larger application around it, we build that too: see custom software development.

By Updated

Discuss an agent How an engagement works

Final frame. Monitor: it is watched after launch, and what is learned goes back into evaluation.

Illustration of an example engagement, with fictional example labels. No results are implied.

What we build

Custom AI agent development: one agent, one job, built around your systems.

Useful agents are narrow. We start from the job, then decide which tools, data and approval steps it needs.

  • Task

    Task agents

    Take one repeatable job from start to finish, such as sorting incoming requests, filling in missing record fields or drafting a report, then hand the result to a person.

  • Research

    Research and analysis agents

    Pull data from several sources, compare it and write up the findings with every source listed, so someone can check the work.

  • Operations

    Operations agents

    Work inside your systems under permissions you set: open tickets, update records, run checks. When a case falls outside the rules, the agent stops and asks.

  • Advertising

    Ad operations agents

    Read campaign performance, draft changes and assemble reports for advertising teams. Changes wait for a person to approve them.

Dependability

What makes an agent safe to use at work.

A demo shows what an agent can do. Limits, tests and records decide whether you can rely on it, and that is where most of the engineering goes.

  • A defined job

    One goal, written down, with a way to tell whether the result is right. A vague goal gives you a vague agent.

  • Narrow tools

    The agent can call only the tools it is given, each with the smallest permission that works. A new ability is a new tool, reviewed on its own.

  • Evaluation cases

    A set of example tasks with known good answers, run before and after every change. If something breaks, a test fails first.

  • Approval and logging

    Actions that change something can wait for a person to approve them. Every call is logged, so any decision can be explained later.

How an engagement works

How an AI agent engagement works, from scoping call to handover.

Each stage ends with something you can look at. The prototype comes before the full build, so you judge the result before you commit to it.

  1. 01

    Scoping conversation

    We listen to the problem, the systems involved and the constraints. You get a written scope, an approach and a way to judge success before work starts.

  2. 02

    Prototype or design

    A small working version or a design that you can test against your own data, so the main risks surface early and cheaply.

  3. 03

    Build

    Development in short cycles with working software at the end of each, reviewed with you.

  4. 04

    Test and harden

    Automated tests, agreed evaluation cases for anything AI-driven, security review of access and secrets.

  5. 05

    Deploy and hand over

    Release to your environment, with documentation, runbooks and a handover session for the people who will run it.

Technology

The stack we build agents on.

Python and Node.js for agent code, MCP server development for tool access, and Cloudflare, AWS or Azure for hosting. We pick the model per project and keep it swappable.

  • Node.js
  • Python
  • Django
  • MCP
  • Cloudflare
  • AWS
  • Azure
  • DevOps

What you receive

  • The agent, deployed in your environment
  • The tools and MCP servers it calls, with the permission each one has
  • The evaluation cases and the results of running them
  • Documentation and a runbook for the people who will operate it
  • A handover session with your team
FAQ

Questions on custom AI agents.

An AI agent development company designs, builds and deploys AI agents for other organisations. Vexio scopes the job and its limits, builds a prototype on a sample of your data, then builds the agent with the tools it may call. We test it against cases you agree, add approval and logging, deploy it in your cloud and hand it over with documentation.

We do not publish a price list. Cost depends on how many tools the agent calls, how clean your data is and which actions need approval. We scope the job in a first conversation and quote against that written scope. The article on custom AI agent development process and cost factors explains what moves the number.

It depends on scope, so we do not quote a standard duration. A short prototype comes first, built on a sample of your data, so you can judge the result before you commit to the full build. The scoping call gives you a timeline for your case.

The model is chosen per project, based on the task, your data-handling constraints and the cloud you run on. We design the agent so the model can be changed later without rewriting the tools around it. Tell us about constraints such as data residency or an approved provider in the scoping call.

By limiting what it can do, not by hoping it behaves. Each tool gets the smallest permission that works, write actions can require human approval, every call is logged, and the agent is tested against agreed cases before and after each change.

Not at the start. A prototype can run on a sample or on redacted data. If production access is needed later, it is agreed in writing with the minimum scope the agent needs to do its job. We can also work inside your environment instead of receiving a copy of the data.

Ownership and licensing terms are written into the engagement agreement before work starts, so ask about them on the first call. The website does not set those terms. Custom projects run under a separate written agreement. If you want something specific, such as owning the code outright, say so early so the scope reflects it.

Ask for a prototype on your own data, a written scope, evaluation cases you agree and the approval rules for any action that changes something. Be wary of a vendor who cannot say what the agent may not do. Our guide on how to choose an AI agent development company lists the questions.

Keep reading
Talk to us

Describe the job you want an agent to do.

Tell us the task, the systems it touches and who reviews the result. We reply by email to set up a scoping call.

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hello@vexio.ai
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