Agentic AI Interview Glossary

The DBA's Guide to Oracle Agentic AI Foundations · Glossary

Every acronym, key idea and likely interview question from the series, explained in plain words with an Oracle DBA comparison. Bookmark it for revision before your exam or interview.

• Full forms
• Key concepts
• Interview Q&A
• How to answer

Full forms

The highlighted letters show where each acronym comes from. Learn the full form first; the meaning usually follows from it.

AI and model basics

AI

Artificial Intelligence

Machines doing tasks that need human-like thinking

ML

Machine Learning

Systems that learn patterns from data instead of fixed rules

GenAI

Generative AI

AI that creates new text, images or code

LLM

Large Language Model

The "brain" of an agent, trained on huge amounts of text

NLP

Natural Language Processing

Making computers understand human language

HITL

Human-in-the-Loop

A person approves each important step

Reasoning and retrieval

RAG

Retrieval-Augmented Generation

Fetch relevant documents first, then let the LLM answer from them

CoT

Chain-of-Thought

Reason step by step before answering

ReAct

Reason + Act

Thought → Action → Observation, in a loop

ToT

Tree-of-Thought

Explore several reasoning paths, keep the best

NL2SQL

Natural Language to SQL

Turn an English question into a SQL query

KB

Knowledge Base

The document library a RAG tool searches

Frameworks and protocols

MCP

Model Context Protocol

Open standard to plug any AI app into any tool ("USB-C of AI")

A2A

Agent2Agent protocol

Open standard for agents talking to other agents

SDK

Software Development Kit

Ready-made libraries for building apps, e.g. OpenAI Agents SDK

LCEL

LangChain Expression Language

Joins steps with a pipe: prompt | model | parser

API

Application Programming Interface

A defined way for software to talk to software

REST

Representational State Transfer

The common style for web APIs over HTTP

JSON

JavaScript Object Notation

The text format tools and LLMs exchange data in

JSON-RPC

JSON Remote Procedure Call

The message format MCP uses (version 2.0)

HTTP

Hypertext Transfer Protocol

Web transport; MCP's remote option is Streamable HTTP

SSE

Server-Sent Events

Older MCP remote transport, replaced by Streamable HTTP

stdio

Standard input/output

MCP transport for a local server on the same machine

OAuth

Open Authorization

Standard for secure delegated access to remote servers

Oracle and database

OCI

Oracle Cloud Infrastructure

Oracle's cloud

IAM

Identity and Access Management

Who can do what in OCI (policies, compartments)

ADB

Autonomous Database

Oracle's self-managing cloud database

HNSW

Hierarchical Navigable Small World

In-memory graph vector index: fastest, very accurate

IVF

Inverted File (Flat) index

Partition-based vector index: scales to huge data

ANN

Approximate Nearest Neighbor

Fast vector search using an index, slightly less than 100% accurate

KNN

k-Nearest Neighbors

Find the k closest vectors; exact search compares them all

ONNX

Open Neural Network Exchange

Model format you load into the database to create embeddings

VPD

Virtual Private Database

Row-level security that also applies to agent queries

PII

Personally Identifiable Information

Names, phone numbers, IDs: guardrails mask or block it

Key concepts

What each term means, and how to explain it using things a DBA already knows.

AI agent

An LLM that plans, uses tools and remembers, looping until a goal is met

The DBA wayThe on-call DBA who fixes the alert, not the one who only explains the error

Workflow (vs agent)

Fixed steps decided by code, with LLM calls inside

The DBA wayA scheduled RMAN script: same steps every time

Token

The small piece of text a model reads and writes (about ¾ of a word)

The DBA wayLike a block: the unit everything is counted and billed in

Context window

How many tokens the model can see at once; also its short-term memory

The DBA wayThe SGA: fast, limited, cleared when the session ends

System prompt

Fixed rules that set the agent's role and limits

The DBA wayThe runbook and change policy

Embedding

A list of numbers that captures the meaning of text

The DBA wayA fingerprint of meaning, not of spelling

Vector

The stored embedding; similar meanings sit close together

The DBA wayA row you search by closeness instead of equality

Semantic search

Finding results by meaning, not exact words

The DBA way"Tablespace full" also finds "no space in datafile"

Cosine similarity

Measures the angle between two vectors; the usual text metric

The DBA wayUsed inside VECTOR_DISTANCE(..., COSINE)

Chunking

Splitting documents into small pieces before embedding

The DBA wayLike splitting a large export into manageable files

Grounding

Tying answers to real data (tools, RAG)

The DBA wayAnswering from AWR, not from memory

Hallucination

A confident but made-up answer

The DBA wayA guess written into an RCA without proof

Function / tool calling

The LLM outputs a structured request; the app runs it

The DBA wayThe LLM writes the ticket; the operator executes it

Guardrails

Checks on input and output (injection, PII, toxicity)

The DBA wayChange control and audit for the agent

Prompt injection

Hidden instructions in input or tool results that try to hijack the agent

The DBA waySQL injection, but in plain language

Least privilege

Give tools only the access they need

The DBA wayA read-only DB user for a reporting agent

Handoff

One agent passes control to another

The DBA wayL1 hands the ticket to L2

Supervisor pattern

A manager agent delegates to specialists

The DBA wayThe incident manager in a P1 war room

Reflection

The agent critiques and improves its own output

The DBA wayReviewing your own RCA before sending it

Fine-tuning

Retraining a model on your data to change its behaviour or style

The DBA wayChanging the engine, not the fuel

RAG vs fine-tuning

RAG adds fresh facts at question time; fine-tuning changes how the model behaves

The DBA wayUse RAG for changing data like runbooks; it needs no retraining

Zero-shot / few-shot

Asking with no examples / with a few examples in the prompt

The DBA wayFew-shot = showing a junior two sample tickets first

Temperature

Controls randomness; low = consistent, high = creative

The DBA wayKeep it low for SQL and factual answers

Observability / tracing

A log of every step, tool call and result

The DBA wayThe audit trail and AWR for agents

Interview Q&A

The 15 questions you are most likely to hear. Each answer takes under 30 seconds to say.

Try answering out loud first, then tap a question to check yourself.

1What is an AI agent?

An AI agent is an LLM that can plan, use tools, remember and act in a loop until it reaches a goal. A chatbot answers once; an agent does the work, like an on-call DBA who checks, fixes and verifies.

2What are the components of an agent?

Five: the model, instructions, tools, memory and planning. I remember them as "My Instructions Tell Me Plans".

3Agent vs workflow: what's the difference?

In a workflow, code fixes the steps. In an agent, the LLM decides the next step based on what it finds. If the steps never change, a workflow is cheaper and more reliable.

4Does the LLM execute the tools itself?

No. The LLM returns a structured request with the tool name and arguments. The application runs it and sends the result back. That is function calling.

5Explain ReAct.

Reason + Act. The agent alternates Thought, Action and Observation until it can answer. It's how a DBA tunes a query: think, run the plan, read it, think again.

6What is RAG and why use it?

Retrieval-Augmented Generation. You retrieve relevant chunks from your documents and give them to the LLM with the question. It grounds answers in your data, reduces hallucination and needs no retraining.

7RAG or fine-tuning?

RAG for facts that change, like runbooks and policies. Fine-tuning to change the model's style or behaviour on a narrow task. Most enterprise use cases start with RAG.

8What is MCP?

Model Context Protocol, an open standard that connects AI apps to tools and data in one way. The host runs clients; each client connects to one server; servers expose tools, resources and prompts over JSON-RPC 2.0. Think of it as the Oracle listener for AI.

9Tools vs resources vs prompts in MCP?

Tools are actions the model decides to call. Resources are read-only context the application supplies. Prompts are templates the user picks.

10How do you build agents on OCI?

Create an agent with instructions, attach tools (RAG for documents, SQL for tables, custom functions for APIs), connect a knowledge base, and expose it through an endpoint with sessions and guardrails.

11What does Oracle AI Database add for agents?

It stores vectors next to relational and JSON data, so one query can combine semantic search and SQL. Existing security like privileges, VPD and auditing still applies, and there is no separate vector store to keep in sync.

12HNSW or IVF?

HNSW is an in-memory graph: fastest and very accurate, but needs the vector memory pool. IVF partitions vectors on disk and scales to very large data with less memory.

13What is Select AI?

Natural language to SQL inside the database, using DBMS_CLOUD_AI and an AI profile. Actions like showsql show the generated SQL, runsql runs it, and narrate answers in plain English.

14How do you make agents safe in production?

Input and output guardrails, least-privilege tools, human approval for irreversible actions, iteration and token limits, and full tracing. The same discipline as change control on a production database.

15Single agent or multi-agent?

Start with one agent. Move to multi-agent (supervisor, handoffs) only when tasks need clear specialisation, because each extra agent adds latency, cost and debugging effort.

How to answer any question

Use the same three steps every time. They show both knowledge and real experience.

Step 1

Define it with the full form

"RAG stands for Retrieval-Augmented Generation. It retrieves relevant documents and gives them to the LLM before it answers."

Step 2

Give the DBA analogy

"It's like answering a performance question from the AWR report instead of from memory."

Step 3

Connect it to Oracle

"In Oracle AI Database, the chunks sit in a VECTOR column, and I find them with VECTOR_DISTANCE using an HNSW index."

Don't know an answer? Say what you do know and how you would find out: "I haven't used that in production, but it works like X, and I'd check the OCI documentation for Y."

Part of The DBA's Guide to Oracle Agentic AI Foundations. For the full picture in one diagram, see the Agentic AI Cram Board. Independent study notes, not official Oracle material.