AI agent development
AI that acts within the limits you set.
Our AI agent development services are for work that runs across several tools. An agent reads an incoming request, finds the customer in your CRM, checks the order and prepares the next step. We agree with you what it may do on its own and what needs a person’s approval, then test the whole chain on your real examples before it touches live data.
What our AI agent development services include
Choosing the task
We pick a recurring task with clear inputs and outputs, and decide which steps need AI and which work better as simple rules.
Permissions and approvals
We set what the agent may read, draft and change in each system, and what a person must approve first.
When things go wrong
Every step is logged. Failed actions are retried safely, duplicates are blocked and unclear cases go to a person.
Pilot and costs
We test on typical and tricky examples and measure how many tasks the agent completes, how much review it needs and what it costs to run.
What you have at the end of the pilot
- A description of the agent’s task and a table of what it may do in each system.
- A pilot in which every agent action is logged and important steps are approved by a person.
- Pilot results (completed tasks, review time and running costs) and a plan for what to improve next.
Do you need an AI agent, or is a simple automation enough?
An ordinary automation follows fixed steps: a form arrives, a deal is created. It is cheap and predictable, and it fails as soon as the input looks different from what the rule expects. An agent gets a goal, tools and limits: it reads the request, chooses which allowed step to take and asks a person when it is unsure.
That flexibility pays off only where inputs really vary, such as free-text emails or requests that need data from several systems. If your process fits a flowchart with no “it depends” boxes, we will suggest a plain integration. It is cheaper to build and to run.
Custom AI agents for business: tasks that suit them
Typical starting points, not client stories:
Triage and preparation
Reads emails or forms, finds the customer and the order and prepares a reply or a task for the right person.
Stuck orders
Finds orders that have waited too long at one stage, looks up why and drafts a message to the customer or the team.
Facts for a decision
Before a manager approves a refund, the agent collects the history and open invoices into one summary with links.
Cross-checks
Compares records between systems and prepares corrections that a person confirms.
What this can look like
An incoming request needs sorting, a CRM lookup and a follow-up task. The agent prepares all three, checks the required fields and asks a manager to approve before it changes the customer record.
Who an AI agent is for
A good fit
- Teams with a multi-step task that repeats every day and runs across two or more tools, such as email, a CRM and a task tracker.
- Processes where incoming information varies and simple rules miss too many cases.
- Companies ready to name a person who reviews the agent’s work during the pilot.
Not the right fit
- Stable, predictable steps. An ordinary integration or rule is cheaper and more reliable: see system integration or business process automation.
- Tasks where a mistake would reach a customer before anyone could check it, and nobody is available to review the agent’s work.
What affects the cost of AI agent development
We quote cost and timing in EUR after reviewing the task and your examples. The quote depends on:
Systems and actions
Reading two systems is a smaller job than writing to a CRM, accounting and email. Each write action needs its own permission, test and undo step.
Variety of inputs
Tidy forms are easier than free-text messages in several languages with attachments.
Approvals and logging
The more expensive a mistake, the more approval steps, logging and safe retries the agent needs.
Running costs
Model usage, hosting and review time. We measure all three in the pilot, so you see the cost per task before expanding.
We’ll quote cost and timing in EUR after a short brief.Discuss an AI agent
Where AI agent projects usually fail
- A broad assistant that “handles operations” instead of one task with a clear finished result.
- Write access everywhere from day one, because approvals feel slow in the demo.
- Tests on tidy examples only, while real requests have missing fields and forwarded threads.
- Nobody on the client side who decides whether the agent was right.
So we build the pilot around these four points: one task, narrow permissions, awkward examples and a named person who judges the result.
How an agent goes from brief to pilot
Each stage ends with something you can check before we continue.
Brief and examples
We agree the task, the systems involved and what a finished result looks like. You send typical and difficult examples.
Test on real cases
We run the agent on your examples without access to live systems and record where it succeeds, hesitates or fails.
Pilot in your tools
The agent handles a small volume of real requests, and a person approves every action that changes data.
Review and next step
We compare the pilot with the current way of working and decide together: widen its permissions, adjust it or stop.
What we need from you
A short description of the task, a few dozen anonymised examples (the awkward ones included), test accounts or read access to the systems involved, and a list of actions the agent must never take.
We also need one person who knows the process well and can say whether a result is right. The pilot is measured against their judgement.
AI makes mistakes, so we don’t promise full automation without review. Where an error is costly, a person approves the result, and confidential data goes only to services your company has approved.
AI agent development: common questions
How do you check that the agent keeps working well?
We keep a set of real cases with expected results and rerun it after every change to instructions, tools or model. In live use we track finished tasks, handovers to people, reviewer corrections and cost per task.
Can an agent act without staff review?
Only for actions you approve in advance. We start with narrow permissions and approval for anything that changes data, and widen them only after the pilot shows the agent works reliably.
How is an agent different from a chatbot?
A chatbot answers questions. An agent also takes actions (creates a record, sends a request, updates a status) within the permissions you give it, and asks a person to approve risky steps.
Can it work with our CRM?
Usually yes, through an API or an integration platform. We check the available connection during the first review.
Read before you start
Short practical guides on the same subject.
- Since 2006
- Written scope before work starts
- One contact person
- Reply within 24 hours
Which multi-step task should an agent handle?
Describe the steps, the tools involved and which actions a person must approve. We’ll reply with questions and a suggested first step.
The first conversation is free, with no obligation.