An AI workflow that can choose its next step.
An agentic workflow connects agents, tools and approvals. AI chooses its next action based on the input and the rules you have set.
What do you get?
You get a repeatable process with a defined trigger, permitted actions and human approval points.
How does it work?
Work starts with a defined trigger
A customer email arrives, a document is added or a scheduled time is reached.
AI chooses an appropriate action
The agent assesses the input and uses permitted tools. It can ask for clarification when information is missing.
The result reaches a checkpoint
The workflow returns an output or routes it for human approval. Its activity history helps explain decisions later.
A simple example
Different customer emails need different responses
AI identifies whether the email requests information or an order change.
An information request gets a draft reply based on your guide.
An order-change request is routed to the responsible employee.
An illustrative example, not a description of a specific client project.
Explore the technical detailsA closer look at roles, rules and checks
What makes a workflow agentic?
Traditional automation follows a pre-defined path. An agentic workflow interprets the input, chooses the next permitted step, uses the right tools and either produces the result or asks a person to decide when the situation is unclear.
How does it work in a company?
For example, an incoming customer enquiry can go to a classification agent, then to an agent that searches your knowledge, then to one that drafts a reply. The workflow sends it to a person for approval. Each role owns one clear part and the whole process remains traceable.
Boundaries before autonomy
Before launch we define which data and tools an agent may use, which decisions require approval and when the work must stop. Agentic does not mean uncontrolled — it means the system can choose the next step inside agreed boundaries.
Agents, subagents and an orchestrator
A simple task may need one agent. More complex work is split between subagents, while an orchestrator makes sure results arrive in the right order. Roles, ownership and the place to fix a problem stay visible.
Measurable and improvable
We test whether the workflow triggers correctly, uses the right sources, produces a useful result and asks for approval at the right moment. Run history shows where it stumbles so we can improve a specific agent or stage.
Where should your company start?
Tell us about one recurring task. We will look at whether and how AI could help.