An autonomous software program capable of completing multi-step goals from start to finish without needing step-by-step guidance.
Unlike standard chatbots that only reply to prompts, an AI Agent can use browsers, query APIs, inspect databases, make branching decisions, and deliver finished results.
A collaborative operating model where repetitive tasks run autonomously via AI agents while human specialists verify and approve critical milestones.
Rather than manual repetitive prompting, agentic workflows execute research, drafting, and code auditing in parallel, presenting ready proposals for human sign-off.
An open standard created by Anthropic allowing AI models to securely connect to external tools, databases, and local enterprise data sources.
Think of MCP as a universal USB plug for AI. Instead of writing custom brittle API integrations for every system, MCP offers a uniform communication bridge.
A method that supplies verified internal company documents to an AI before generating answers, preventing fact hallucination.
RAG functions like an open-book exam for AI models. Before producing prose, the AI queries your vector database for verified passages and cites them accurately.
A deep learning neural network trained on vast text corpora to understand nuances, synthesize data, and generate natural language.
LLMs power modern assistants such as Claude, ChatGPT, and Gemini, handling complex translation, code synthesis, reasoning, and document analysis.
The discipline of designing structured instructions, context bounds, and constraints to produce consistent enterprise-grade AI outputs.
Beyond basic conversational prompts, this involves system roles, few-shot examples, chain-of-thought sequencing, and schema validation enforcement.
The fundamental unit of characters or word segments processed by language models to compute context limits and API costs.
Roughly 1,000 tokens represent 750 English words. Both context window boundaries and API usage bills are calculated strictly by token consumption.
When an AI model produces confident, convincing statements that are factually fabricated or unsupported by reality.
Hallucinations happen because LLMs predict next-token probabilities rather than querying an infallible truth store. RAG and human oversight are vital mitigations.