MNB Solutions atAI & Big Data Expo Europe 2026in Amsterdam

We are going for a very specific reason: to compare how enterprises are moving AI and agentic systems from impressive demonstrations into controlled, economically sensible production.

That means talking about the parts that rarely make it onto the demo screen - architecture, data access, permissions, traceability, human intervention, monitoring and accountability.

The production problem is getting more interesting

Building an agent that can complete a task is becoming easier. Building one that can operate inside a regulated enterprise is not.

Once AI can retrieve company data, call systems, initiate workflows or make decisions, the architecture around the model becomes just as important as the model itself.

• ✦Who can the agent act for?
• ✦Which systems can it access?
• ✦What happens when confidence is low?
• ✦Where does a human intervene?
• ✦Can every important action be reconstructed later?
• ✦And does the economics still make sense once integration, monitoring and exception handling are included?

That is the conversation we want to have in Amsterdam.

Four conversations we are bringing to Amsterdam

1. 01

   Agentic systems with controlled autonomy

   The interesting question is not how autonomous an agent can become. It is where autonomy creates measurable value and where permissions, validation or human approval need to stop it.

   We are working on architectures that make those boundaries explicit rather than leaving them inside a prompt.

2. 02

   Architecture before orchestration

   The model is only one component. Identity, enterprise data, APIs, legacy systems, permissions, observability, fallback behaviour and audit trails determine whether an agentic system can eventually operate reliably.

   That architecture is where much of the real engineering sits.

3. 03

   Governance that reaches the code

   ISO 42001 and the EU AI Act should not end as policies and PowerPoint decks. Governance has to become implementation: defined responsibilities, testing, approval gates, traceability, change records and operating controls.

   MNB Solutions uses ISO 42001 internally as an operating framework for AI governance rather than treating it as a marketing badge.

4. 04

   AI economics beyond token cost

   A cheap model does not automatically produce a cheap system. The real economics include integration, infrastructure, monitoring, human review, exception handling, maintenance and the manual work actually removed.

   That is the ROI conversation worth having.

If you are building AI that eventually has to survive real enterprise operations - security reviews, legacy systems, messy data, audit questions and human accountability - we should compare notes in Amsterdam.

✦ When and where

19–20

October

2026

RAI Amsterdam

AI & Big Data Expo Europe - part of TechEx Europe.