AI agents that work with your systems.
An agent differs from a chatbot in that it has access to your systems and permission to act: find, calculate, file, hand over. We design such agents around a specific process — with explicit authority boundaries, logs and cost control.
What we do
Break the process into steps
First we map what a person actually does by hand and where time is lost. The agent is built for that route, not for “AI in general”.
Tools and system access
The agent works through tools: CRM, knowledge base, mail, payments, internal services. Each tool is an explicit contract, not blanket access to everything.
Authority limits and a human in the loop
What the agent does alone, what it sends for approval and what it never touches is decided up front and lives in code, not in a prompt.
Observability and cost
Step-level logs, quality evaluation on your scenarios, token-cost monitoring. You can see what the agent did and what it cost.
From an AI prototype to a working scenario
Your prototype answers test examples, but launch still needs integrations, quality checks and failure handling. We will examine one scenario and agree acceptance criteria and the scope of the first release.
Agree the result
We describe the user journey, how the task is handled today and the result the first release must deliver. We also agree how to check the value added by AI.
Check quality and cost
We select real examples and refusal conditions. We agree acceptable wait times, attempt limits and the cost of a completed action.
Connect systems and check failures
Within the agreed scope, we connect the required data and actions. We check delays, repeated requests, result persistence and handover to a person on failure.
An example from our work
In our own product, a review found a risk of two replies to one message. We separated model retries from saving and notification. An injected-failure check produced one saved reply and one notification per message.
The example concerns a pre-release check. It does not include failure-rate measurements from real traffic. We will estimate your project scope, timeline and price after examining the scenario. Start by describing your prototype and what is preventing launch.
Why us
FAQ
How is an AI agent different from a chatbot?
A chatbot replies with text. An agent performs steps in your systems: it finds the data, calls the right tools and takes the task to a result, not to a reply.
What if the agent does the wrong thing?
That's why authority limits live in code: irreversible actions go through human approval, everything else is logged step by step and can be traced.
Do we need an agent or is plain automation enough?
If the steps are fixed, ordinary automation is cheaper. An agent pays off where judgement is needed: parsing an unstructured request, choosing a route, gathering data from several places.
Which models does it run on?
Chosen for the task and data constraints: external LLM providers or self-hosted models, weighing privacy and cost.
What does it cost to run?
Cost comes from request volume and context length. We estimate it before the start and build in limits and monitoring so spend is never a surprise.
Read also
AI agents for business: which tasks to delegate
AI agents for business can help with workflows that involve interpreting text, choosing a next step and using several systems. Start with one process and define its result, permitted actions and acceptance checks. The scenarios and selection table below help you choose a pilot.
How much does an AI agent cost?
An AI agent's cost includes development and the ongoing cost of a working process. A model tariff covers only part of that total: integrations, retries, maintenance and human review also matter. Before comparing proposals, define the result you are paying for.
OpenAI Agent Builder is shutting down: how to migrate an agent without breaking it
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PDF, 2 pages. No sign-up, no email — just the file.