As excitement around Agentic AI accelerates, it’s worth separating what looks impressive from what actually holds up in regulated, high-risk environments. From experience, three foundations will matter most:
1. Hybrid GenAI + Symbolic AI is non-negotiable
Mission-critical agents cannot rely on probabilistic reasoning alone.
Symbolic AI provides accurate, deterministic, and auditable reasoning.
GenAI excels at user interaction and extracting nuanced attributes and entities from natural-language documents and communication.
The future is not one or the other — it’s deliberately combining both.
2.Decision-centric symbolic AI beats knowledge graphs for automation
For applications that require real decision-making and automation, decision-centric symbolic AI (decision trees, decision tables) is essential.
Compared to data- or entity-centric knowledge graphs, decision models offer:
Clear traceability
Explainable outcomes
Regulator-ready auditability of the reasoning process
When accountability matters, decision structure matters.
3. GenAI assisted decision modelling becomes the real breakthrough
The most powerful shift will happen at design time, not runtime. GenAI will partner with human experts to:
Generate decision models from documents / SOPs etc.
Validate them
Accelerate deployment
At runtime, those models will execute with predictable accuracy and full auditability — the standard required for regulated domains.
The takeaway:
GenAI is transformative — but only when grounded in architectures that prioritize control, transparency, and trust that Symbolic AI provides.
We're curious to hear how others are approaching regulated AI beyond the hype.