Practice 04 · Artificial intelligence & automationAI agent development: work that gets done, not just questions that get answered
A chatbot answers a question. An AI agent takes on a goal, looks up what it needs, picks the sequence of actions required and carries them out across your software, one step at a time. The challenge shifts from answering well to acting well, and to deciding what the agent may do on its own versus what it must hand to a person.
A quick analysis of a concrete case, no commitment
Inside an agentHow an AI agent works
An agent combines a language model with memory, tools and the ability to plan. How those building blocks are put together and governed is what makes it reliable, and that is exactly what we design with you.
AI agent
Memory
The context of the task at hand, its history and your company’s data.
- Working memory
- Long-term memory
Tools
The applications the agent can access, read-only or with write access.
- CRM & ERP
- Document search
- Email & calendar
- Business APIs
- … your software
Planning
How the agent breaks the goal down, chooses the sequence of actions and checks its own work.
- Breaking down into steps
- Reasoning
- Self-checking
- Rules & thresholds
Action
Execution in your software, with a readable log of every step.
Human approval
Sensitive actions wait for a person’s approval before they run.
Who it is for, and whenWhere an AI agent earns its place
Agents fit tasks that chain reading, deciding and acting across several systems, where each occurrence resembles the last without being identical to it. A fixed rule calls for standard automation. The moment someone has to interpret context before choosing what to do, an agent becomes the better fit.
- A request arrives by form or email: find the matching record in the CRM, complete the file and schedule a follow-up.
- Monitoring tenders: collect, filter against your criteria, draft a summary sheet and file it in your tracking tool.
- Matching orders to delivery notes: spot discrepancies and prepare a draft message to the supplier.
- Sorting applications or technical tickets: read, categorise, enrich, then route to the right person.
The scopeWhat Kopus builds in an agent project
Most of the work happens before any code is written: mapping the agent’s territory. Which tools it can reach, with read-only or write access, and which actions it may take alone versus which wait for approval.
From there we develop the orchestration (the logic that sequences the steps), the connectors to your applications, and we select the language model, for example a model accessed by API or one hosted on your own infrastructure when your data requires it. Every run produces a readable log, and a supervision interface gathers the actions awaiting approval along with the cases where the agent chose to stop.
Before go-live, a set of scenarios drawn from real cases measures the share of correct executions, which determines what the agent can be trusted with unsupervised.
The methodHow we build an agent, step by step
Task breakdown
With the people who do the job today, we describe every step, every intermediate decision and every screen they open.
Permission design
What the agent reads, what it writes, and the thresholds beyond which it must stop.
Proposal-only prototype
The agent reasons over real cases but executes nothing, and your teams judge its choices.
Supervised launch
Live execution on a narrow scope, log review, adjustment of instructions and thresholds.
Gradual expansion to new cases or new actions, as observed results justify it.
Our positionOur view: an agent should never act alone on what matters
Language models are sometimes wrong with complete confidence. In a chatbot, a mistake is fixed by rephrasing; in an agent, it can trigger a payment, a message or a deletion. Any sensitive action (financial commitment, external communication, change to contractual data) must therefore go through a person, and the agent must be able to say it does not know. The time saving is still real: the agent prepares, documents and proposes, and the human decides in seconds.
Your questionsFrequently asked questions about this service
What is an AI agent in a business context?
A system that receives a goal and performs a series of actions to reach it, within a set of tools and permissions defined in advance. Unlike a chatbot, it can act inside your applications.
What happens when the agent meets a case it does not understand?
It stops, records what it understood and passes the case to a person through the supervision interface. That behaviour is designed in from the start.
What does running an agent cost?
Each run consumes calls to the language model, billed on usage. We measure that unit cost during testing and compare it with the human time saved before rolling out more widely.
What is the difference between an AI agent and a chatbot?
A chatbot converses; an agent acts within your tools: it reads, writes, triggers actions. Many projects combine both, and we help you choose the right level of autonomy.
Is our data used to train models?
No. We configure the services used so that your data is neither retained nor used for training, and we favour European hosting where possible.
How long does it take to set up an AI agent?
Four to eight weeks for a first agent on a precise use case, including a test phase on real cases. The scope is widened afterwards, once reliability has been measured.
Go furtherPages related to this service
- Your challenges · 03I want to bring AI into my companySpot the AI uses that make sense and keep control of decisions.
- Practice 04Artificial intelligence & automationUse new technologies to simplify operations, save time and create new opportunities. We integrate AI where it does a concrete job, with your teams in control.
- 04 · AI & automationBusiness process automationEvery company has processes everyone does by hand because “it has always been this way”. We spot them, simplify them, then automate them, with or without AI depending on what is most reliable.
- 04 · AI & automationAI API integrationAI models have become software building blocks like any other, provided you know how to choose, integrate and monitor them. We add AI capabilities to your existing applications, cleanly, without weakening them.
Let’s bring your projects to lifeYour next project starts here.
Let’s look at the task you would hand to an agent, and how far you would let it go.
A quick analysis of a concrete case, no commitment