Automation & AI
AI Agents
They interpret, decide and act on your systems.
If you need to solve tasks that require interpreting and deciding —not just executing—, an agent fits: from serving your customers connected to your systems, to supporting internal processes like audit, reconciliation or analysis.
An agent goes beyond executing fixed steps: it understands context, decides and acts connected to your tools. It can serve your customers with real access to your systems, or support internal processes, always with human checkpoints where judgment matters.
What's included
- Agent design and its boundaries
- Connection to your systems (ERP, CRM, databases)
- Human checkpoints where judgment matters
- Outcome measurement
Agent governance
Do you know what your AI agent decided today, and who approved it?
Most companies deploy agents that reason, decide and act inside their processes with no governance structure behind them. If you can't answer these four questions, you have a black box making business decisions.
- Why did it make that decision?
- What data did it use?
- Can it be reversed if it was wrong?
- Did anyone approve it?
central_log
- 09:14agent-01reconciles #0842Supervisor
- 09:16agent-01approves paymentSupervisor
When it fails, it doesn't fail once
One error repeats across hundreds of cases before anyone notices. Without a log there's no evidence for an audit and no way to explain it in time.
The model we work with: four pieces
Gradual autonomy
The agent first proposes, then operates supervised, and only after proving consistency earns real autonomy. Autonomy is earned, not given.
Filters before acting
Every action passes confidence, bias, policy and compliance checks before it touches a system.
Central log
If an action isn't logged, it's out of policy. That's what lets you audit, explain and reverse.
Human in the loop
Mandatory in sensitive, regulated or ambiguous cases. Judgment stays with a person.
It's not about slowing innovation down: with control, you automate faster.
Other solutions
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