The work proposes a five-layer architectural framework that embeds governance and security requirements throughout system design rather than treating them as separate concerns.
A new approach is needed, one that thinks in terms of dynamic spectrums rather than static boxes.
Companies invest heavily in AI tools they don't understand, creating procurement and implementation challenges for product and legal teams managing vendor relationships and technology integration.
Are you building supervision frameworks that match the level of autonomy you're granting? Treating agents like assistants when they're acting like employees doesn't just create compliance risk—it creates the kind of accountability vacuum that ends badly.
Agentic AI demands a different approach to governance—proactive, structured, layered.
Agentic AI fails due to unrealistic expectations about automation capabilities, poor use case selection, data quality problems across multiple sources, and governance gaps requiring custom solutions.
Companies seeing real returns from AI agents build measurement systems alongside the technology, treating deployment as architectural decisions rather than bolt-on solutions.
I keep seeing teams conflate AI agents with agentic AI, and this distinction matters more than most realize. One's a contained service; the other's a network of intelligent actors making collective decisions.
