01
The task has several dependent steps
The system must collect data, assess context, use a tool, verify the result and only then perform the next action.
AI / tasks, tools and control
AI agents for business with approvals, logs and human control
We design AI agents for defined tasks with scoped data, tools, rules, approvals, logs, exception handling and a safe human fallback.
01one clearly defined outcome instead of unbounded autonomy
02permissions, approvals and limits before an action is executed
03a complete operation trail and human handoff for exceptions
AI development servicesBusiness context
An AI agent should execute a defined process, not make unlimited decisions.
An agent connects an AI model with tools and sequential steps. It can read data, prepare a proposal, call an agreed action or send the case for approval. The value comes from the process, not from the agent label itself.
We limit scope, data and permissions first. Reversible actions can become more automated, while financial, legal, publishing or access-related operations should have explicit thresholds and human control.
Where value leaks
When a chatbot or a single automation is no longer enough
The first useful scope comes from the real bottleneck, not from a prebuilt package.
02
Exceptions require a different path
The process cannot safely end with one response. An unusual case should reach a defined owner with the full context.
03
Nobody can tell what the AI did
Without logs, task identifiers and action history, it is difficult to explain an outcome, correct a failure or understand operating cost.
04
The model has excessive permissions
Access to many systems and operations without limits increases the risk of an unwanted action, data exposure and costly escalation.
Implementation scope
An agent as a controlled part of the business workflow
We build the scope around the task, tools and boundaries of responsibility. Autonomy is earned through evidence and risk review, not enabled by default.
Task and success definition
We document input, expected output, permitted decisions, prohibited cases and measurable quality criteria.
Tools and integrations
We connect agreed APIs, CRM, knowledge sources, email or panels only within the permissions the task needs.
Roles and permissions
We limit access to data and actions, separate environments and define who can trigger each operation.
Approvals and limits
We add thresholds, budgets, blocks and human confirmation before higher-risk actions can execute.
Logs and exception handling
We record steps, tool results, failures and human handoffs without exposing sensitive data where it is not required.
Evaluation, monitoring and cost
We test normal, boundary and adversarial cases, then observe quality, exception volume and execution cost after launch.
Delivery
An agent pilot should solve one measurable task
Each stage has a decision, an output and a clear reason to move forward.
01
Process selection
We choose a repeatable task with available data, a clear owner and an outcome that can be measured.
02
Boundary design
We define tools, permissions, decisions, approvals, budget, logs and the fallback route.
03
Pilot and evaluation
We run the agent across a case set, measure step correctness and analyse every exception.
04
Controlled expansion
We broaden the scope only after quality is demonstrated and the human fallback remains reliable.
Expected outcomes
What a sensible agent implementation should produce
one task has a defined beginning, end and owner
the agent uses only the data and tools it needs
higher-risk actions require approval
every operation leaves an inspectable trail
exceptions reach a human with the full context
quality, cost and manual intervention volume are measured
AI agent pricing / net prices
We choose the task and autonomy level first, then estimate a safe pilot.
The price covers more than a model: tools, data, tests, approvals, logs and a human fallback path. The more responsibility an agent receives, the more control the implementation requires.
| Option | Price | Scope | Included |
|---|---|---|---|
| AI opportunity audit | from PLN 900 net / one-off | The typical range is PLN 900–2,500. We organise the process landscape and choose a task that creates business value and can be tested safely. |
|
| AI agent connected to systems | from PLN 12,000 net / implementation | The entry point for one controlled workflow with agreed tools, logs, approvals and a human fallback. |
|
| Monitoring and ongoing care | from PLN 900 net / month | The existing post-launch care threshold for an agent. Scope depends on run volume, integration changes and the expected response target. |
|
| Advanced agent system | Custom quote agreed individually | Multiple agents, multiple systems, high-risk actions, complex approvals, high volume or enhanced SLA requirements. |
|
Net prices in PLN. “From” means the smallest sensible first-stage scope. Advanced delivery — including multiple integrations, migrations, custom roles and permissions, local or hybrid environments, high volumes, extended SLAs or business-critical workflows — is quoted individually after discovery or an audit.
Evidence
We do not publish fictional stories of autonomy
The current portfolio shows AI modules and working software, but it does not prove a production agent executing actions inside a client system.
Decision support
Related layers of a controlled AI implementation
AI development services
The parent offer for audits, chatbots, RAG, agents and other business AI scenarios.
Open resourceSystem and API integrations
The technical layer needed when an agent must read data or perform actions inside company tools.
Open resourceBusiness process automation
See when predictable rules are safer and less expensive than using an AI model.
Open resourceFAQ
Questions before the first scope
Clear answers before technology, timing and budget are committed.
How is an AI agent different from a chatbot?
A chatbot mainly holds a conversation and provides answers. An agent can execute a multi-step task and use tools, so it requires stronger permissions, logs, limits and controls.
Can an agent send emails or update CRM data?
It can technically do so, but the scope should follow the risk. It is often safer to begin with a draft that requires approval, then automate reversible actions after evidence is collected.
Can the agent operate without a person?
We do not treat full autonomy as the goal. The process should define when the agent acts, when it asks for approval and when it must hand the case to a person.
How do you limit harmful actions and hallucinations?
Through a restricted tool set, output validation, grounded knowledge, rules, evaluations, budgets and approvals. The risk cannot honestly be reduced to zero.
How much does an AI agent cost?
An AI opportunity audit starts from PLN 900 net, while an AI agent connected to systems starts from PLN 12,000. Monitoring and ongoing care start from PLN 900 per month. Multi-agent workflows, high-risk actions and complex approvals are quoted individually.
Which process should we start with?
Choose a repeatable, moderate-risk task with available data and a clear correctness criterion, such as lead qualification, draft preparation or evidence collection for a decision.
Next step
Choose one task that currently crosses several tools
Describe the input, sequential steps, people who approve decisions and systems used along the way. We will assess whether you need an agent, standard automation or a lighter integration.
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