Services
AI agents that work inside your tools
AI agents connected to the tools you already use, with clear limits, tests that run before every change, and a person approving anything risky.
AI agent development is building software in which a language model reads information and takes actions inside your own tools, within written limits. Vyorde builds agents and the harness around them, meaning permissions, tests, logs and approval steps, for companies that want AI on real work and need to know what it did.
What you get
Agents in your tools
An agent that reads and acts in your CRM, inbox, help desk or database, through their APIs.
Clear limits
A written list of what the agent may do on its own, and what it may never do.
Human approval
Risky steps, like sending money, deleting data or emailing a customer, wait for a person to say yes.
Evaluations
A set of test cases from your real work, run before every change, so you can see if it got better or worse.
Logs
A record of what the agent saw, what it did and why, so a mistake can be traced instead of guessed at.
Data map
A plain list of which data the agent touches, where it is sent and how long it is kept.
What's included
- One task chosen and written down, with a description of what a good result looks like
- A set of test cases taken from your real work
- The agent connected to your tools with the narrowest access that does the job
- Approval steps for anything that is hard to undo
- Logs of what the agent saw, what it did and why
- A data map: which data goes to which provider, and how long it is kept
- Documentation on how to change the limits and rerun the tests
What's not included
- Training a model of your own. We build on existing models from established providers.
- Fixed, rule-based workflows that do not need a model at all. That is Automation, and it is cheaper and more predictable.
- A general AI plan for the whole company. That is closer to the Fractional CTO package.
You'll probably like this if
- Your team answers the same twenty questions every day, and the answers are already written down somewhere.
- You tried an AI tool, it was right most of the time, and nobody could tell which time it was wrong.
- You want to use AI on company data, and you need to know where that data goes.
How it goes
We pick one task.
Frequent, dull and easy to check. We collect real examples and agree what a good result looks like.
We build the agent and its harness.
Tools, permissions, approval steps and logging, with the narrowest access that does the job.
We test it on your cases.
Against the examples we collected, then alongside your team, where a person reviews every output.
We widen it carefully.
Only the steps that passed the tests get more freedom. Everything else keeps a person in the loop.
Every project follows the same five steps. See the full process.
What changes the price
- How many tools the agent connects to
- How many steps need human approval
- How sensitive the data is
- How many test cases we build and maintain
Tell us what you need and you get a quote in writing before anything starts.
Before you start
- One task that is frequent, dull and easy to check
- Real examples of that task, with the answers your team gave
- A list of the tools involved and who can grant access to them
- The actions that must always wait for a person
- Any rules about where your data may be sent or stored
You don't need all of it for the first call.
Questions about AI agents
Will it make mistakes?
Yes, sometimes. That's why we test it on your real cases, log what it does and keep a person in charge of anything that's hard to undo.
Where does our data go?
We tell you before anything is built: which model provider sees what, under which terms, and whether it can be kept in the EU. If the answer isn't good enough, we change the design.
Can it replace a person?
Usually it takes over the repetitive part of someone's job, so they spend their time on the cases that need judgement.
How long does it take to build an AI agent?
We start with one task, and a first version of that is a small project. Most of the time goes on collecting real examples and testing against them, not on the agent itself. Widening it to more steps happens afterwards, in stages, and only for the steps that passed. Dates are in the written quote.
What do you need from us to start?
A set of real examples of the task, with the answers your team gave. Access to the tools the agent will use, through accounts with limited rights. Someone who knows the task well enough to judge whether an output is right. And a decision on what the agent must never do.
Whose account pays for the AI model?
Yours. The account with the model provider is opened in your company's name, so usage is billed to you directly and the terms are between you and them. The instructions, test cases, code and logs are yours once the work is paid for. The test cases are also what make it possible to try a different model later.
Does an AI agent need maintenance?
Yes. Model providers release new versions and retire old ones, your tools change, and new kinds of cases turn up. Each time, the test cases are run again before anything goes live, which is what they are for. Your own developers can do that from our documentation, or we can under a support agreement.
More questions are answered on the FAQ page.
Not sure where to start?
Try the Fractional CTO package first. For companies without a senior technical person who keep making technical decisions anyway, and would rather not guess. Typical timeline: 2 to 3 weeks to get up to speed, then month to month. The exact dates are in your quote.
From Insights
Often paired with
- 03AutomationHand the copy-paste work to a machine.
- 01Custom SoftwareCustom software for when spreadsheets give up.
- 08Maintenance & SupportMonthly care for the things you already have running.
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