Process description
Goal, inputs, outputs and exceptions of the process, agreed with the business.
AI & automation
AI agents for clearly defined business processes
Agents with defined tools, execution limits and traceable logging.
In short
Ki4data builds AI agents for clearly bounded business processes: with defined tools and actions, abort conditions, business approval where needed, and logging that makes every step traceable. An AI agent works through a task in several steps, such as a search, a database query or creating a case.
Agent, chatbot, RAG or classic automation?
A chatbot answers questions in a dialogue. RAG answers questions based on your documents. Classic automation runs a fixed, rule-based sequence. An agent decides within set limits which tools to use and in what order, which makes it suitable for tasks with variable intermediate steps.
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Typical starting points
- Staff research recurring cases across several systems and document the result by hand.
- Cases must be prepared from unstructured input such as emails or documents.
- Bounded internal tasks follow a recognizable pattern but have too many variants for fixed rules.
What you receive
Tool and interface inventory
Which systems the agent may read or change, and with which rights.
Execution limits
Permitted actions, abort conditions and steps that require business approval.
Tests
Test cases for standard and exception paths, rerun before every change.
Logging and operational handover
A traceable record of every step and a documented operating model.
How we work
Choose the process
Rate candidates by value, risk and data availability.
Set limits
Define tools, rights, approvals and abort conditions.
Prototype
Build the agent for the standard case and test it with real examples.
Harden
Add exception handling, error handling and logging.
Roll out
Introduce it into the process step by step, starting with business approval.
Execution criteria
- Permitted actions are explicitly defined.
- Exceptions are defined and lead to a handover to people.
- Abort conditions trigger on uncertainty or errors.
- Critical steps require business approval.
Prerequisites and limits
A good fit when …
- The process is clearly bounded and has a verifiable result.
- The required systems are reachable through interfaces.
- Business contacts can assess results and set limits.
Another solution fits better when …
- The process is fully deterministic; classic workflow automation is usually more reliable and cheaper.
- Errors could not be corrected and no human approval is planned.
What drives effort and cost
Every project is scoped individually. These factors influence the effort most:
- Number and accessibility of connected systems
- Number of variants and exceptions in the process
- Approval, logging and audit requirements
- Usage volume and chosen model in production
Frequently asked questions
Which processes suit AI agents?
Bounded tasks with variable intermediate steps, such as research across several sources with a documented result, or preparing a case from unstructured input. Processes where errors are hard to spot or cannot be corrected are not a good fit.
How do we stay in control?
Through defined tools and rights, abort conditions, business approval for critical steps and logging of every step. The agent can only do what it is explicitly allowed to do.
Is an agent always the best solution?
No. For deterministic sequences, rule-based automation is usually more economical. We check this before building and recommend the simpler route when it is enough.
How do we get started with AI agents?
With one well-bounded process that has a verifiable result. The agent first runs with business approval for every critical step; logs and test cases show where it works reliably before approvals are reduced step by step.
Sources and vendor documentation
Related services
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Next step
In a first conversation we clarify your situation, goals and a sensible starting point. Afterwards you receive an assessment of what a project could look like.
Discuss automation potential