Your Engineering Team Is Speaking a New Language: An Executive's Plain-English Guide to AI Development

I was explaining our AI development approach to a senior executive last week when he stopped me mid-sentence. "I don't know what an MD file is," he said. "What's a skill? When you say 'context,' what are you talking about?"

He wasn't being difficult. He was being honest. And that honesty revealed a problem hiding in plain sight across every enterprise AI initiative I've been part of.

The people making multi-million-dollar decisions about AI strategy often can't follow the conversations where those strategies get shaped. Not because they lack intelligence. Because the AI industry has built its own dialect, and nobody wrote a translation guide.

This is that guide.

The vocabulary gap is a strategy gap

A 2025 survey by Lucidworks found that 72% of C-suite executives feel confident about their organization's AI strategy. But when researchers tested those same executives on basic AI concepts, the results told a different story. Most couldn't accurately explain how the tools they'd approved actually work.

This isn't an academic problem. When you can't distinguish between a large language model and a fine-tuned model, you can't evaluate whether your team's proposed approach is sound. When you don't know what a context window is, you can't understand why your AI assistant sometimes forgets what you told it five minutes ago. When "agent" means nothing more specific than "smart bot," you'll overpay for simple automation dressed up in agentic packaging.

The vocabulary gap isn't about buzzwords. It's about decision quality.

How AI-assisted development actually works

Before we get to the glossary, here's the 60-second version of how AI fits into software development today.

Your engineering teams used to write every line of code by hand. Then autocomplete tools started suggesting the next few characters. Now, AI can write entire functions, review code for security problems, generate test cases, and even architect solutions based on a description of what you want built.

Think of it like the evolution of manufacturing. Artisans making everything by hand gave way to power tools, then assembly lines, then robotic automation. The humans didn't disappear. Their role shifted from manual labor to design, oversight, and quality control.

That's exactly what's happening in software development. Your best engineers are becoming orchestrators who direct AI tools rather than writing every line themselves. The ones who adapt are delivering work 60–80% faster. The ones who don't are writing code the same way they did five years ago.

Your job as a leader isn't to understand the technical details. It's to understand enough to recognize where your organization sits on that spectrum, and what it takes to move forward.

The glossary: 24 terms you'll actually hear

I've organized these into four categories that map to how you'll encounter them in practice: the foundational concepts, the development terms, the enterprise and strategy layer, and the quality and safety terms.

The foundation: How AI thinks

Artificial Intelligence (AI)
Software that performs tasks typically requiring human judgment. In business context today, this almost always means software that understands and generates language, images, or code. When your teams say "AI," they usually mean the next term.

Large Language Model (LLM)
The engine behind ChatGPT, Claude, and Copilot. An LLM is a program trained on enormous amounts of text that can understand questions, generate responses, write code, and analyze documents. Think of it as a very well-read colleague who has studied millions of books and conversations but has never actually worked at your company.

Generative AI (GenAI)
AI that creates new content: text, code, images, presentations. This is the umbrella term for the current wave of AI tools. Every time someone asks ChatGPT to draft an email, they're using generative AI.

Token
The unit of measurement for AI text processing. Roughly one token equals one word, though some words get split into multiple tokens. Why this matters to you: AI services charge by token count, and every model has a maximum number of tokens it can handle at once. When your team talks about cost optimization, they're often talking about reducing token usage.

Context window
The amount of information an AI can hold in its "working memory" during a single conversation. Think of it as the AI's desk. A bigger desk lets it spread out more documents and reference more material. When someone says a model has a "200K context window," they mean it can work with roughly 200,000 words at once. When the desk fills up, older information falls off the edge.

Prompt
The instruction you give an AI. "Write me a summary of this report" is a prompt. "Review this code for security issues" is a prompt. The quality of the output depends heavily on the quality of the prompt, which is why "prompt engineering" became a discipline.

Inference
When an AI generates a response, that process is called inference. This is the operational cost of running AI. Model training happens once (and costs millions). Inference happens every time someone asks the model a question (and costs fractions of a cent). Your cloud bills for AI are mostly inference costs.

Hallucination
When an AI generates information that sounds correct but is factually wrong. The model isn't lying. It's filling in gaps with statistically plausible text. This is why human review remains essential for anything consequential. If someone tells you their AI "never hallucinates," they're either confused or selling you something.

The development layer: How teams build with AI

Copilot
Microsoft's branding for AI assistants embedded in their products (GitHub Copilot for code, Microsoft 365 Copilot for office work). The term has become semi-generic, the way "Kleenex" stands for tissue. When your teams say "copilot," they mean an AI assistant that works alongside them inside the tools they already use.

Agent / AI Agent
An AI that can take actions on its own, not just answer questions. A chatbot waits for you to ask something. An agent can browse the web, call APIs, modify files, run code, and chain multiple steps together to complete a task. An agent is the difference between a librarian who finds books for you and an assistant who researches, summarizes, and drafts the report.

Multi-agent
Multiple specialized AI agents working together on a task. One agent handles requirements analysis. Another writes code. A third reviews it for security issues. A fourth runs tests. This mirrors how human teams divide labor, except agents can run in parallel and hand off work without scheduling meetings.

Skill
A packaged set of instructions that teaches an AI agent how to perform a specific task the way your organization does it. Without skills, an agent writes code in a generic style. With your company's skill loaded, it follows your naming conventions, uses your preferred frameworks, and applies your security standards. Skills are how you turn a general-purpose AI into one that understands "how we do things here."

Orchestration
The act of coordinating multiple AI agents, tools, and workflows to accomplish a complex task. Your senior engineers are increasingly becoming orchestrators rather than hands-on coders. They design the workflow, assign agents to each step, set quality checkpoints, and validate the results.

Markdown
A simple text formatting language that uses symbols instead of toolbar buttons. A hashtag (#) makes a heading. Asterisks make text bold. Dashes create bullet lists. AI tools use markdown extensively because it's lightweight and readable by both humans and machines. When your team mentions markdown, they're talking about a plain text format, not a programming language.

MCP (Model Context Protocol)
An open standard that lets AI agents connect to external tools and data sources. Think of it like USB for AI. Before USB, every peripheral needed its own proprietary cable. MCP does the same for AI integrations: one standard protocol that connects any AI model to any tool, database, or service.

RAG (Retrieval-Augmented Generation)
A technique that lets AI pull in relevant information from your company's documents before generating a response. Instead of relying only on what the model learned during training, RAG searches your knowledge bases in real time. This is how you make AI answers specific to your organization without retraining the entire model.

Fine-tuning
Retraining an AI model on your company's specific data so it performs better on your use cases. This is more expensive and complex than RAG but produces a model that deeply understands your domain. Most organizations start with RAG and fine-tune only when they've proven the use case justifies the investment.

The strategy layer: Enterprise AI decisions

Prompt engineering
The discipline of crafting effective instructions for AI. Early AI adoption treated this as an art. It's now becoming a systematic practice with documented patterns and measurable outcomes. Good prompt engineering is the difference between AI that produces generic output and AI that delivers exactly what you need.

Context engineering
The practice of controlling everything an AI can see when it processes a request: the prompt, the documents, the conversation history, the organizational knowledge. If prompt engineering is writing a good question, context engineering is preparing the entire briefing package. This is where the real enterprise value lives.

Specification (in agent development)
A document that describes what success looks like rather than step-by-step instructions. Traditional requirements tell developers how to build something. Specifications for agents describe the desired outcome, constraints, and quality criteria, then let the agent determine the best approach. Writing good specifications is becoming one of the most valuable skills in engineering.

Agentic AI
AI systems designed to act autonomously toward goals. Rather than answering individual questions, agentic AI breaks down complex objectives, creates plans, executes steps, evaluates results, and adjusts course. This is the frontier of enterprise AI, where the technology moves from assistant to operator.

The safety layer: Quality and risk

Guardrails
Rules and constraints that prevent AI from doing things it shouldn't. Content filters, spending limits, approval gates before actions are taken, restrictions on what data the AI can access. Guardrails are how you maintain control while giving AI enough autonomy to be useful. More guardrails means more safety but less speed. Finding the right balance is a leadership decision.

Grounding
Connecting AI outputs to verified information sources. An ungrounded response comes purely from the model's training data (which may be outdated or wrong). A grounded response cites specific, verifiable sources. Grounding is how you reduce hallucinations and make AI outputs trustworthy enough for business decisions.

Human-in-the-loop
A workflow design where humans review and approve AI decisions at critical points. Not everything needs human approval (an AI can auto-format code without asking). But security changes, financial transactions, and customer-facing content should have a human checkpoint. The question isn't whether to include humans but where in the process they add the most value.

Putting it all together: A real-world scenario

The glossary gives you definitions. But the real value is understanding how these pieces connect. Let me walk you through a scenario your teams are probably living right now.

Imagine your engineering team gets a request: build a new customer portal that lets clients track their order status, contact support, and manage their account. Here's how AI-assisted development handles this, with every term from the glossary showing up naturally.

The starting point. A senior engineer sits down with an AI agent, not a simple chatbot, but one that can take actions, read files, and execute multi-step work. The engineer writes a prompt: "Build a customer portal with order tracking, support chat, and account management. Follow our company's security and design standards."

That prompt is short. But here's where context engineering comes in. The agent doesn't just see those two sentences. It also loads the team's skills, pre-packaged instructions that encode how this company builds software. One skill says "we use React for frontends." Another says "all customer data must be encrypted at rest and accessed through our API gateway." A third contains the company's design system with approved colors, fonts, and component patterns.

All of that—the prompt, the skills, the conversation history, the referenced documents—fills the agent's context window. Think of it this way: if the context window is a desk, the prompt is the sticky note with today's assignment. The skills are the company handbook, the style guide, and the security policy. The referenced codebase documentation is the stack of technical drawings. Everything has to fit on that desk at the same time, and the desk has a fixed size.

This is why tokens matter to your budget. Every word in those skills, every line of referenced documentation, every back-and-forth message in the conversation consumes tokens. A skill that's 3,000 tokens is taking up desk space that could be used for the actual work. Multiply that across dozens of agents running hundreds of requests per day, and token efficiency directly affects your cloud spend.

Where context limits hit. Here's the moment that catches most teams off guard. The engineer asks the agent to review the entire existing codebase (150,000 lines) alongside the new portal requirements. But the agent's context window is 200,000 tokens. The codebase alone consumes 120,000 tokens. The skills take another 15,000. The conversation history is 10,000. That leaves just 55,000 tokens for the agent to actually think and generate code. Performance drops. The agent starts "forgetting" instructions from earlier in the conversation because they've fallen off the edge of the desk.

This is why your team talks about RAG (retrieval-augmented generation). Instead of cramming the entire codebase into the context window, RAG lets the agent search for just the relevant files when it needs them. The agent pulls in only the authentication module when working on login, only the order database schema when building the tracking page. The desk stays clean. The agent stays sharp.

The multi-agent workflow. Now the work splits across specialized agents in a multi-agent setup. One agent analyzes the requirements and produces a technical specification. A second agent takes that specification and generates the code. A third agent reviews the code for security vulnerabilities. A fourth writes and runs tests.

Orchestration is the act of coordinating all of this. The senior engineer isn't writing code line by line. They're designing the workflow: which agent handles which task, what quality gates sit between each step, where a human-in-the-loop checkpoint is needed. Security changes? Human approval required. Color adjustments to a button? The agent handles it autonomously.

The connection layer. The portal needs to pull order data from the existing ERP system and send support tickets to the CRM. This is where MCP (Model Context Protocol) comes in. Rather than building custom integrations for each AI tool, MCP provides a standard interface. The agent connects to the ERP through an MCP server the same way it connects to the CRM—one protocol, many connections, like USB for AI.

Quality at the end. Before anything ships, guardrails catch problems. The agent can't deploy directly to production without approval. It can't access customer financial data outside the approved API. Grounding ensures that when the agent generates documentation for the portal, it cites actual API endpoints and real database fields rather than hallucinating plausible-sounding but nonexistent ones.

What the executive sees. From your chair, here's what happened: a feature that used to take a team of five developers three months was delivered in three weeks by two engineers orchestrating AI agents. The code follows your company's standards (because those standards were encoded as skills). The security review was thorough (because a specialized agent ran checks against your actual compliance requirements). And the cost was measurable in tokens consumed, agent time used, and human hours for oversight.

That's the full picture. Every term in the glossary has a role. None of them exist in isolation. And when your team throws these words around in their next briefing, you'll know exactly how the pieces connect.

How to use this in your next meeting

You don't need to memorize these definitions. You need to know enough to ask three questions:

"What's the context window for our current setup, and are we hitting limits?" This tells you whether your teams are constrained by the AI's working memory, which directly affects output quality.

"Where are the human-in-the-loop checkpoints, and why there?" This reveals how much autonomy the AI has and whether the risk controls match your tolerance.

"What skills have we built, and what institutional knowledge are we still missing?" This tells you whether your AI investment is accumulating organizational value or starting from scratch every time.

Those three questions will give you more strategic insight than any vendor demo.

The real risk isn't the technology

Every technology wave comes with its own vocabulary. Cloud computing brought us "elastic scaling" and "microservices." Mobile brought us "responsive design" and "push notifications." Leaders figured those out because they had to.

AI's vocabulary wave is bigger, faster, and more consequential. The decisions being made right now—about agents, context engineering, multi-agent orchestration, and skill development—will shape your technology organization for the next decade.

You don't need to write code. You don't need to understand neural network architecture. But you do need to understand the language well enough to evaluate whether the strategy your team is proposing will actually work.

The executives who learn this vocabulary won't just follow the conversation. They'll lead it.

References

1. Lucidworks. "2025 AI Benchmark Survey: Executive AI Literacy." 2025.
2. McKinsey & Company. "The state of AI: How organizations are rewiring to capture value." 2025.
3. Anthropic. "Model Context Protocol." modelcontextprotocol.io
4. Anthropic. "Introducing Agent Skills." October 2025. anthropic.com
5. Microsoft. "GitHub Copilot Enterprise." github.com

This article is part of "The Agent-First Enterprise" series exploring how organizations can transform their operations around AI agent capabilities. Connect with me on LinkedIn or Substack to continue the conversation.

Matthew Kruczek

Managing Director at EY

Matthew leads EY's Microsoft domain within Digital Engineering, overseeing enterprise-scale AI and cloud-native software initiatives. A member of Microsoft's Inner Circle and Pluralsight author with 18 courses reaching 17M+ learners.