The more vendors claim to be agentic, the harder it is to find the ones that actually are.

Agentic AI was easily the #1 topic in every CIO/CDO/architect conversation I had at Symposium.

I can group the conversations into three buckets:

1. How do we even think about agents?

Are they employees? Tools? Something in between?

People phrased it differently, but the underlying tension was the same: if agents are going to take on real work, what expectations should we set around reliability, autonomy, traceability, escalation, handoffs, and accountability?

It feels less like adopting a tool and more like hiring a teammate with very specific strengths, and, like any colleague, areas to improve.

2. Everyone is now an "AI company."

Half the market rebranded itself "agentic" overnight.

Platforms that never touched AI are suddenly "agentic." RPA tools are now workflow-plus-LLM engines. Every CRM/ITSM/HCM platform is suddenly "AI-native."

It's created a strange paradox: the more vendors claim to be agentic, the harder it is to find the ones that actually are. It reminds me of the conversational AI wave in 2020, when every platform magically had a "virtual agent" plugin, but 80% weren't production-ready. I fear a similar fate for these "agentic" platforms.

So the real question becomes: how do I ask the right questions during vendor demos so I avoid multi-million-dollar mistakes down the line?

3. What does a real Agentic AI architecture look like when you pull back the marketing slides?

A lot of decks collapse "agentic" into a single box labeled "agent"… or my new least favorite phrase, "agentic orchestrator."

The reality is always in the boring scaffolding underneath: persistent memory, retrieval strategies, safety layers, action tooling, human-in-loop triggers, evaluation loops, and the actual workflow design pattern behind autonomous tasks.