I sat in a meeting last week where four people used the word "agentic" in two minutes.
Three of them couldn't have defined it on a quiz.
This is not new. AI has had a vocabulary problem since the first time someone said "neural" and meant something other than the inside of a brain. But the rate has accelerated. There is now a buzzword for every layer of the stack, every quarter, every conference. By the time you have learned what RAG means, the deck has moved on to graph-RAG, agentic-RAG, and self-improving-RAG, and the meaning has eroded under each one.
I am tired of it. Not because the words are silly — most of them point at real things — but because the words have become a tax. They are how people sound like they know things while not having to know them. They are how decks get approved. They are how budgets get funded. They are how products get shipped that nobody is held accountable for, because nobody can quite say what they do.
A buzzword's job is to let a room nod at something it could not, if pressed, draw on a whiteboard.
I want to make the case for the opposite: operational language. The kind that survives the meeting it is spoken in. The kind a junior engineer can use, an auditor can review, a customer can verify, and a regulator can interpret in their own jurisdiction. That language exists. It is not glamorous. It is also the only language I have ever seen produce a working enterprise AI system.
The bingo card
Here is the card I have been mentally crossing off in conference sessions and earnings calls for the last six months. If you hear any five in a row from one speaker, congratulations, you have a bingo. You also have permission to stop listening.
What these words actually mean — and what they usually don't
It is worth being precise about the eight phrases I find most often hide behind their own glow. None of these are wrong, exactly. They are worn. The same word does heavy lifting in twenty different rooms and means something slightly different in each one, and the listener is never quite sure which definition they just bought.
| The phrase | What it should mean | What it usually means in 2026 |
|---|---|---|
| Agentic | A system that takes a goal, picks tools, executes steps, observes results, and adjusts. A model with a tool-use loop, plus telemetry. |
Anything that calls an LLM more than once. Often: a chatbot with a function-call button. |
| AI-native | Designed from the ground up assuming AI capability and inference cost as first-class constraints. Choices about latency, eval, and trust drove the architecture. |
"We added a chat input to our existing product." Sometimes: "we mentioned AI in the seed deck." |
| Foundation model | A large, general-purpose model trained on broad data, used as a base for downstream applications. Llama, GPT, Claude, Gemini, Qwen, Mistral, DeepSeek. |
Anything large enough to brag about. Sometimes a fine-tune of a foundation model. |
| Enterprise-grade | SSO, audit logs, RBAC, SLA, data residency, and incident response that hold up under a regulator's pen. A specific operational list, checked by procurement. |
"We have a SOC 2 in progress." Often: a sales adjective with no list attached. |
| Production-ready | Has run in production, has incident history, has a rollback plan, has an on-call. Has survived day 60 in someone else's environment. |
The demo worked once on the founder's laptop. Sometimes: "we deployed it to a staging URL." |
| Reasoning model | A model whose architecture or fine-tuning meaningfully improves multi-step inference, with measurable benchmark improvement. A specific class of model trained to plan. |
"We added 'think step by step' to the prompt." Often: chain-of-thought re-marketed. |
| Disruptive | Reshapes the basis of competition in a market — usually by attacking the dominant player's economics from below. A Clayton Christensen sense of the word. |
"We have a website and a pitch deck." Sometimes: literally any new feature. |
| Hallucination | A model output that is fluent, confident, and not grounded in the source material — a specific failure mode of generative systems. Distinguishable from staleness, retrieval errors, or alignment drift. |
Anything the model got wrong. Often used as a catch-all for "we don't know why it broke." |
The practitioner's filter — seven questions
Here is the filter I run in real time when somebody pitches me on a buzzword-heavy AI product, paper, or initiative. None of these are clever. All of them work. The reason they work is that they require the speaker to translate their term into an operational claim — a claim that could, in principle, be falsified.
If a question can't be answered, that is the answer.
What does the loop look like in practice — not in the diagram?
The diagram is the marketing artifact. The loop is the truth. Ask for the actual sequence: trigger, inputs, model call, tool calls, evaluation, output, feedback. If the answer is a circle with arrows and no nouns, you're being sold a vibe.
Who pays — and what specifically do they get for it?
Ask the boring procurement version. What's the SKU. What's the unit. What's the price. What happens when it breaks. If you cannot name the buyer, the product, the price, and the SLA in one sentence each, you are looking at a category, not a company.
Can you describe it to a nine-year-old?
This is the cruelest, most useful test. If the answer requires three more buzzwords, the speaker doesn't understand it either. The point isn't to dumb things down. The point is that real understanding is portable across vocabularies.
What does it look like on day 60 — not at the demo?
Most AI products are demo-shaped. They look extraordinary in a controlled environment and unrecognizable two months in. Ask for the day-60 story. The hardest part of enterprise AI is not building it. It is keeping it standing.
Who is responsible when it gets something wrong?
Every AI system fails. The interesting question is not whether it fails but who owns the failure. If the answer is "the model" or "the user" or "nobody, it's just AI," the system is not ready to ship. A named owner of harm is the most boring and most important governance artifact in the field.
What's the eval — and who designed it?
"It works" is not an eval. Ask for the actual test suite. Who wrote the prompts. What domain. What's the ground truth. What's the false-positive rate. What's the cost of a miss. If the answer is "we just felt it was better," the model is not being measured. It is being marketed.
If you remove the AI, does the product still solve a problem?
This is the deepest question. Many "AI products" are real products with AI bolted on; many are AI features hunting for a problem. The difference shows up in the answer to: if the model were a junior intern instead of a frontier LLM, would the product still matter? If yes, it's a real product. If no, you're building on sand.
Worked example — applying the filter to "agentic"
Here is the filter running on the buzzword that gets misused most in my week. I do this in my head; I'm doing it on the page so you can see the gears.
"Yeah, we have an agentic platform that —"
Why this matters
Buzzwords are not a cosmetic problem. They are an architectural problem.
When a team adopts a fashionable term they cannot define, the term begins to do work the team is not aware of. It shapes the roadmap. It shapes which engineers get hired. It shapes which features get prioritized. The vocabulary precedes the architecture and then constrains it, often badly. Six months later the company has shipped a product that fits the vocabulary, and nobody quite remembers why the vocabulary was right in the first place.
The cure is not pedantry. The cure is insistence on operational language. The same conversation, conducted with words that have referents in the real world, produces a different product. Better hires. Tighter scope. Clearer evals. Fewer rewrites. Real customers.
Language is architecture. The words your team uses to describe what you're building, you are building. Choose them with at least as much care as you would a database.
What I'd ask instead
For each of the eight buzzwords above, here is the question I'd actually want answered in their place. None of these are catchphrases. All of them are testable.
- Instead of "agentic," ask: what tools does the agent call, in what order, with what stopping condition?
- Instead of "AI-native," ask: what did you design differently because inference is not free?
- Instead of "foundation model," ask: which model, what version, what context window, what license?
- Instead of "enterprise-grade," ask: what's in your SOC 2 scope, what's your data-residency story, who's your auditor?
- Instead of "production-ready," ask: name a customer in production, what's their incident count this quarter?
- Instead of "reasoning model," ask: on which benchmark, by how many points, with what compute budget?
- Instead of "disruptive," ask: whose revenue line does this attack, and on what cost curve?
- Instead of "hallucination," ask: what's your grounding pipeline, and what's the rate at which the model contradicts retrieved sources?
None of these read well in a tweet. All of them work in a meeting.
The closing argument
The companies that are going to ship the consequential AI of the next five years are the ones whose internal language is more boring than their pitch deck. Not because boring is good in itself — it isn't — but because boring language survives contact with reality. It survives the auditor. It survives the regulator. It survives the customer who actually has to use the thing on a Wednesday afternoon when the model is having a slow day.
I am not asking the industry to retire its vocabulary. Vocabularies are how a field thinks. I am asking the practitioners — the people in this audience, the PMs and engineers and clinicians and founders — to start treating their own language as part of the work. To notice, when they catch themselves saying "agentic" out loud, whether they could draw the loop on a napkin if pressed. To notice, when they catch themselves nodding to "enterprise-grade," whether they could name the controls.
If you can, keep the word. If you can't, find a better one. The whole field gets sharper, fastest, when each of us refuses to be impressed by a phrase we cannot define.
That's the work. The bingo card is mine. Make your own.