Most AI vendors go quiet when you stop watching the demo and start asking real questions. That silence tells you everything.
We recently saw a simple challenge on LinkedIn to AI vendors: explain what an agent actually is.
This wasn't a trick question. It's the most basic thing a buyer should understand before writing a check. And yet, across dozens of sales cycles, we keep seeing the same pattern — polished decks, impressive demos, and a fundamental inability to articulate what's actually under the hood.
The AI market is flooded with tools that have "AI-powered" stamped on every slide. Their demos look great. But one honest question exposes the gap between marketing and real capability. Most buyers don't know enough yet to call it out.
That needs to change. Here are four questions we believe every organization should be asking AI vendors — and why the answers matter more than any demo you'll ever see.
This is the filter that separates builders from resellers.
If a vendor can't articulate the difference between autonomous reasoning and a predetermined workflow, they're selling a label, not a capability. A true AI agent makes decisions, routes work based on context, and escalates intelligently when it hits the boundaries of its competence. A workflow with a fresh coat of AI paint just follows the same rigid rules it always did — it's a chatbot in a nicer suit.
As one enterprise AI consultant put it during a recent industry discussion: if a vendor can't explain the system in plain language, they probably don't understand it deeply themselves. Demos are easy. Production-ready, scalable systems with measurable results are rare.
At Dyntyx, this distinction is the foundation of everything we build. Our agents don't just respond — they coordinate across tools, make contextual decisions, and hand off to humans only when it actually matters. That's not a marketing claim. It's the architecture.
When you ask this question, watch what happens. Some vendors refuse to answer. Others mumble about "proprietary AI." If they built something real, they'd know exactly what's running under the hood — and they'd be proud to tell you.
But here's the deeper issue most buyers miss: what happens when you want to switch models?
This might be the most important follow-up question in any AI evaluation. If the answer is "you can't" — or the vendor looks confused — you're staring at vendor lock-in. You'll be trapped in their platform with no exit ramp as the model landscape evolves around you.
The smart play is to own the integration layer and rent the AI model. OpenAI, Claude, Gemini — the best model for a given task changes constantly. Your platform should let you swap freely.
Dyntyx was built from the start to never lock you in. Our agent workflows run on Solis.ai — a platform we co-founded specifically to enable multi-model orchestration. That means we can swap the underlying model for any individual subtask based on what performs best. A summarization step might run on one model while a complex reasoning chain runs on another. We pick the best tool for each job, not the one that keeps you paying us. Multi-model capability isn't a nice-to-have — it's how production AI actually works when you're optimizing for outcomes, not vendor relationships.
Model Context Protocol (MCP) lets AI tools connect to your existing systems and pull context from your own environment. Open APIs ensure your tech stack stays yours. These aren't niche technical details — they determine whether your AI investment integrates with your business or creates a new silo.
If a vendor looks blank when you mention MCP, they haven't thought about how their tool fits into your reality. And an isolated AI tool loses most of its value. The entire point of deploying agents is to eliminate fragmentation — not add another disconnected layer to it.
We've seen too many organizations invest in AI only to discover they've bought something that can't talk to their CRM, their project management tools, or their existing data infrastructure. At Dyntyx, our core principle is orchestration, not fragmentation. Our agents coordinate across email, chat, CRM, and project tools as a single coherent workflow. That requires open architecture from day one — not as an afterthought bolt-on.
This is where the math gets uncomfortable for vendors who'd rather not talk about it.
Hidden costs bury firms that skip this question. Token usage is the meter running in the background of every AI interaction, and most buyers don't know it exists until the first invoice arrives. You need to get the math before you get the pitch.
The reality is that tokens are typically the least-discussed and most impactful cost in any AI deployment. Even as per-token prices drop, total cost of ownership can surprise you — especially when agents are running autonomously across high-volume workflows. If a vendor won't walk you through their token economics in plain terms, that's not an oversight. It's a strategy.
At Dyntyx, we ship every project with explicit KPIs: hours saved, SLA improvement, error reduction. Our clients typically save 20–30 hours per week across automated workflows. We make the economics transparent because the ROI speaks for itself — and because you can't optimize what you can't measure.
These four questions will filter out the majority of vendors who are riding the AI hype wave without real depth. But the sharpest buyers we work with go further.
They ask about governance and security controls — how is data accessed, what audit trails exist, and what happens when an agent acts on sensitive information? These aren't optional features. They're table stakes. Every Dyntyx deployment ships with full audit trails, clear escalation rules, and transparent reasoning — because an agent you can't govern is an agent you can't trust at scale.
They ask about silent failures — the critical signals that get buried when you slap an "AI workflow" label on a process. Human operators used to catch edge cases, flag anomalies, and escalate the things that didn't fit neatly into a decision tree. If your AI vendor can't explain how they surface those signals instead of hiding them, you're trading visibility for automation. That's a bad trade.
And they ask about fit — not "can your product do X?" but "do you actually understand our infrastructure, our use case, our industry?" Too many vendors show up with generic demos that have no connection to the buyer's real environment. At Dyntyx, we build vertical AI solutions tailored to specific sectors — legal, financial services, healthcare, real estate — because an agent that doesn't understand your domain is just another tool that needs babysitting.
You don't need twenty questions to vet an AI partner. You need four good ones — and then you need to listen carefully.
The vendors worth working with welcome these questions. They're proud of their answers. They'll walk you through the architecture, explain the model choices, show you the cost structure, and demonstrate real integration with your tools.
The ones who squirm? That tells you everything.
Dyntyx was built for exactly this kind of scrutiny. We started from a simple frustration: most AI projects never made it out of slide decks. Teams ran pilots, wrote strategy documents, and bought licenses, but the day-to-day work still ran on copy-paste, endless approvals, and manual follow-ups. We build AI agents that actually act — triggering workflows, updating systems, and handing off to humans only when it really matters. Production-ready in weeks, not quarters. Measurable impact from day one.
If you're evaluating AI vendors right now, start with these four questions. And if you want to see how we answer them ourselves — let's chat.
