Fixed rules
Predictable logic with exception checking.
AI assistant
The system proposes; an employee assesses and acts.
AI agent
The system performs steps within explicit rights and boundaries.
Escalation
Any doubts, deviations or higher impact will be referred to a competent person.
Fallback
The process remains safe when model, link or data fails.
Assistant, agent and fixed automation rule
Fixed rules execute pre-written logic. An AI assistant helps search, structure or propose information, while an employee retains the decision and action. An agent can independently perform multiple steps within assigned rights and limits.
These forms require different control. With an assistant, the risk often lies in blind faith in the proposal. With an agent there are additional rights, chain effects and speed. Control must therefore depend on potential impact and recoverability.
Place approval at the right decision point
Not every step needs human approval. Place control where information is uncertain, where an action becomes externally visible, where money or rights change, or where recovery is difficult. Routine low-risk steps can be automated when exceptions are reliably recognized.
An approval screen should show relevant context: source data, proposed action, reason, uncertainty, possible consequences and alternatives. Without that information, human control becomes a formality.
Sources for this section: European Union · NIST
Logging, traceability and rights make control workable
Record which input and version led to a proposal, who approved, what action was taken, and what exception occurred. Logs must be usable for operational monitoring, quality control and incident investigation, without unnecessarily retaining personal data.
Limit permissions according to the task. A system that prepares a draft email does not necessarily send them. An agent reading data does not need to delete it. Where possible, separate reading, proposing, approving and executing.
Sources for this section: NIST · European Union · Data Protection Authority
Design exceptions and fallback before go-live
Determine what happens in case of missing data, low certainty, conflicting instructions, link failure or unexpected volumes. A safe fallback could mean that the task goes to an employee, that only a draft is saved or that the workflow stops completely.
Don't just test normal cases. Use representative exceptions and check whether employees understand notifications, respond in a timely manner and have sufficient authority.
Illustrative example: a customer follow-up agent
A fictitious organization wants an agent to prepare follow-up emails. The assumption is that contact details and conversation summaries are reliably available. In the first phase, the agent only creates concepts; an account manager checks recipient, content and timing.
Once validated, simple reminders can be sent automatically within strict rules. New proposals, sensitive topics and unclear contact status remain subject to approval. Sent messages, source information and exceptions are logged; in the event of failure, the workflow switches back to manual follow-up.
What does this mean for your organization?
Reexamine processes in which AI output directly leads to communication, financial action, granting access or a decision about people. Make impact, uncertainty, recoverability and responsible role visible per step.
Don't automate yet when no one owns exceptions, logs are missing, or reviewers don't know what to look for. Start with one workflow and design doubt, error, escalation and stop in addition to the normal route.
Sources for this section: European Union · European Union
Frequently asked questions
Does a human need to approve every AI outcome?
No. Control should be proportionate to risk, impact, uncertainty and recoverability. Low-risk steps can be automatic when limits and exceptions are reliable.
What is an effective approval?
The reviewer is given relevant context, clear criteria, sufficient time, and the authority to modify, deny, or escalate.
What information belongs in logs?
At a minimum, the relevant input, system or model version, proposed and executed action, decision maker, time and exception route — with respect for data minimization.
Who is responsible for an agent?
Designate a process owner for outcome and exceptions, plus technical and data officers for operations, rights and monitoring.
How do you test fallback?
Simulate missing data, low certainty, unavailable links and unexpected volumes. Verify that the workflow stops safely or escalates correctly.
Sources
The sources below support the indicated factual and regulatory passages. The practical decision frameworks are professional recommendations from DSC Solution.
- European Union — Regulation (EU) 2024/1689 — AI ActJune 13, 2024Gebruikt voor: risk-based obligations, documentation, monitoring and AI literacy.
- NIST — Artificial Intelligence Risk Management Framework (AI RMF 1.0)January 26, 2023Gebruikt voor: risk management, governance and the cyclical measurement and management of AI risks.
- NIST — AI RMF Playbookaccessed August 29, 2026Gebruikt voor: practical actions around Govern, Map, Measure and Manage.
- European Union — Regulation (EU) 2016/679 — General Data Protection RegulationApril 27, 2016Gebruikt voor: principles of lawful processing, data minimization, accuracy and automated decision-making.
- Data Protection Authority — Information brochure about artificial intelligence systems and the GDPRDecember 2024Gebruikt voor: Belgian points of interest for AI and personal data.
