What is agentic AI, actually?

"Agentic AI" gets used loosely, so it's worth being precise about what it actually means — especially before deciding whether to trust one with a real business process.

The three-part test

A regular AI tool responds to a prompt and stops. You ask, it answers, the interaction ends, and if anything needs to happen next, a person has to make it happen. An agentic system is different in three specific ways:

It plans. Given a goal — "qualify this inbound lead" — it breaks that goal into steps rather than waiting to be told each one. It decides, on its own, that qualifying a lead means checking company size, checking budget signals, and checking timeline, in roughly that order, without a human writing out each step in advance.

It acts. It doesn't just suggest a next step, it executes it: sends the follow-up, updates the record, routes the conversation — inside real systems, not just in a chat window. This is the part that actually changes the economics of automation, because a tool that only suggests still needs a human to carry out every suggestion.

It checks its own work. The better implementations verify what they did before moving on, and know when to hand off to a human instead of guessing. This is the quietest of the three capabilities and the easiest to skip when building one — which is exactly why it's the one worth scrutinizing most closely in any product you're evaluating.

Why the third point is the one that decides whether you can trust it

That third point is the one worth paying attention to when evaluating any agentic product, including our own. An agent that plans and acts but never checks is just automation with a hair-trigger — fast, and confidently wrong in exactly the situations where being wrong is expensive. It'll qualify a bad lead as a great one with the same confidence it qualifies a genuinely great one, because nothing in its design distinguishes "I did this correctly" from "I did something."

An agent that plans, acts, and verifies — with a real handoff point to a person when its own confidence drops — is the version actually worth trusting with real business processes. The verification step doesn't need to be exotic: it can be as simple as re-checking output against a rule before committing it, or flagging low-confidence decisions for a human to glance at instead of auto-approving everything. What matters is that the check exists at all, consistently, not just in the demo.

What to actually ask a vendor

When someone pitches you an "agentic" product, the plan/act framing is usually obvious from the demo. The verification step almost never is, because it's the boring part to show off. Ask directly: what does this system do when it's not sure? If the honest answer is "it proceeds anyway," you're looking at automation with extra confidence, not agentic AI worth trusting unsupervised.

That's the bar EBM Iris Orchestrate is being built to — plan, act, and verify, with a real handoff point to a person, not just the appearance of autonomy.