You've been in at least three meetings this month where someone said "agentic" like it was a personality trait. You nodded. We all nodded.

This is the glossary nobody gave you. Every difficult AI term, one plain English line, the exact sentence someone drops in a boardroom, and a straight verdict on whether they actually know what they're talking about.

The Building Blocks: What AI Is Actually Made Of

LLM

Autocomplete that went to college and now runs half the internet.

"We're building on top of an LLM layer with domain-specific fine-tuning applied downstream."

Technically accurate. But anyone can say this without meaning much. Push them on what the LLM is actually doing.

Foundation Model

The base AI everything else is built on top of.

"We evaluated three foundation models before selecting the one best suited to our risk profile."

Solid. Shows they did an actual evaluation rather than just picking the most famous one.

Fine Tuning

Training the same AI harder on your specific industry data.

"We fine-tuned the model on our proprietary data so it understands our workflows natively."

Nine times out of ten, this is actually RAG being mislabelled. Ask them to clarify. The answer will tell you everything.

Tokenisation

AI reads in chunks called tokens, and you get billed for every one of them.

"We optimised our prompt templates to reduce token count by 40% without quality loss."

This one checks out. Someone actually thought about cost efficiency rather than just throwing tokens at the problem.

Embeddings

How AI converts words into math so it understands meaning, not just spelling.

"High dimensional embedding vectors power the semantic search."

Correct but unnecessarily complex way to say it. They could just say semantic search works well.

How AI Thinks and Responds

Context Window

How much information the AI can hold and reference in one go.

"With a 200k context window, we can pass the entire document set in a single inference call."

Fair point, though bigger context windows don't automatically mean better answers. Quality of what goes in still matters.

Temperature

The dial that controls how creative or predictable the AI responses are.

"We've set temperature to 0 for compliance use cases to ensure deterministic outputs."

Good call for compliance. Shows they thought about the use case rather than leaving defaults on.

Inference

The moment the AI actually runs and produces an output.

"Our inference latency is under 2 seconds end to end at P95."

Specific, measurable, honest. This is what good technical communication looks like.

Zero Shot / Few Shot

Whether you give the AI examples before asking it to do something, or just throw it in cold.

"We're using few shot prompting to improve output consistency across edge cases."

Genuinely good practice. Not everyone thinks to do this.

Hallucination

When the AI makes something up and presents it as fact.

"The model was not giving accuracy. It was giving fiction with citations."

Everyone understands this one because everyone has been burned by it at least once.

Latency

How long the AI takes to respond, and in live products every second counts.

"We've optimized our inference pipeline to bring P95 latency under 1.8 seconds."

Under 2 seconds is the threshold where it stops feeling like waiting. This person knows that.

How AI Finds and Uses Your Data

RAG

Your AI connected to your actual company files so it answers from real data, not memory.

"We've implemented RAG so the model is grounded in proprietary data rather than hallucinating outputs."

This is the right approach for enterprise. If a vendor isn't doing this, that's a red flag worth raising.

Vector Database

Where the AI stores meaning so it finds relevant content even when the words don't match exactly.

"Our vector DB enables semantic retrieval across 10 million documents with sub second latency."

Technically sound. Though the 10 million number is doing a lot of heavy lifting here.

Grounding

Making sure AI answers come from real data, not guesswork.

"Grounding the model in real time data sources eliminated hallucinations in our pilot."

This is what separates a demo that works from a product that works. Grounding matters more than most features.

Chunking

Splitting documents into smaller pieces so the AI can search them properly. Bad chunking = bad answers.

"We implemented semantic chunking instead of fixed size chunking to preserve document context."

If someone said this unprompted, they know what they're doing. Most teams skip straight past chunking and wonder why results are poor.

Semantic Search

Search that actually gets what you're looking for, even if you didn't use the right words.

"Semantic search replaced keyword search across our knowledge base. Recall improved by 60%."

Ask to see that number. 60% is specific enough to either be real or made up, and you'll know pretty quickly which one it is.

Multimodal

AI that doesn't just read text. It handles images, audio, scanned forms, video — all of it.

"Our multimodal pipeline processes scanned documents, voice notes, and structured data in a single workflow."

For banking and insurance this actually matters. Most of your data isn't clean text — it's PDFs, call recordings, and handwritten forms.

How AI Actually Takes Action

Agentic AI

AI that goes and does the thing instead of just telling you about the thing.

"We're moving from generative to agentic workflows across the enterprise."

Everyone is saying this right now. Ask them to show you one workflow, start to finish, where the agent actually did something without a human stepping in.

AI Agent

An AI with a specific job, the right tools, and permission to act.

"Our agent handles end to end loan processing without human intervention at each step."

Big claim. Don't let it live on a slide. Ask for a live demo on a real use case, not a sandbox with clean data.

Orchestration

What keeps multiple AI agents from stepping on each other while they work.

"Our orchestration layer handles multi agent task delegation dynamically."

The real question is what happens when one agent in the chain fails. That answer tells you whether they've actually built this or just described it.

Tool Calling

When the AI reaches out to an external system mid-task to get or do something.

"The agent uses tool calling to pull live customer data before generating the summary."

This is how agents actually become useful rather than just impressive in a sandbox.

Multi Agent System

Multiple AI agents working together, each owning a different part of the workflow.

"Our multi agent system handles document intake, risk scoring, and escalation in parallel."

Parallel processing across agents is where real time savings come from. This is worth understanding deeply.

MCP (Model Context Protocol)

A universal standard so AI agents can plug into any tool without custom integrations.

"We're MCP native so integration lift is minimal across the stack."

Worth verifying. MCP is real and useful but it's also becoming a thing people say to sound current.

Prompt Engineering

Writing better instructions to get better outputs from AI.

"Our prompt engineering framework ensures consistent quality across all use cases."

Real discipline, real impact. Just don't let anyone charge you a lot of money for it as a standalone service.

How Enterprises Control and Scale AI

Human in the Loop

Keeping a real person in the process before AI takes any high-stakes action.

"We maintain human in the loop for all high value transactions above the risk threshold."

Non-negotiable for regulated industries. If a vendor is trying to remove this entirely, that's a conversation worth having carefully.

Guardrails

The rules that stop AI from going off script in ways that matter for compliance.

"Our guardrails prevent the model from generating outputs outside regulatory boundaries."

Ask what the guardrails actually cover. A list of blocked words is not a compliance framework.

AgentOps

Watching what your AI agents actually do once they're live, not just in testing.

"AgentOps gives us full observability into every agent action across our deployed workflows."

Someone who volunteers this word has probably broken something in production and learned from it. That's the person you want building your system.

AI Governance

The rules, roles, and audit trails that stop AI from becoming a liability.

"Our AI governance framework ensures model decisions are explainable and auditable."

Every vendor has a governance slide. Ask what happens when the model gets it wrong. That conversation is more revealing than any framework document.

STP (Straight Through Processing)

A transaction that goes all the way through with no human touching it.

"We improved STP rates from 42% to 78% within the first quarter of deployment."

STP is one of the few AI metrics that ties directly to real money saved. If someone quotes a number like this, it's worth asking how they measured it.

The Bottom Line

Enterprise AI isn't complicated. The vocabulary around it is. And that gap between what vendors say and what actually gets built is where bad decisions get made. Now you have the cheat sheet. The next time someone drops "agentic orchestration with multi-modal RAG grounding" in a meeting, you'll know exactly what to ask.

The best AI question you can ask isn't technical. It's simple: show me what it actually does.

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