AI Agent Glossary

Master the language of AI agents. Clear definitions, real examples, and technical depth—no fluff, just what you need to build and deploy faster.

Agent Architecture

Agent architecture is the system design behind an AI agent, defining how it perceives inputs, reasons about decisions, uses tools, stores memory, and operates within business guardrails.

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Agent Memory Systems

Agent memory systems enable AI agents to store and recall past experiences across sessions, improving decision-making through context retention, pattern recognition, and personalized responses based on historical interactions.

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Agent Observability

Agent observability is the practice of tracing, measuring, and debugging AI agent behavior across prompts, tool calls, retrieved context, outputs, latency, and cost.

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Agent Orchestration

Agent orchestration coordinates multiple specialized AI agents within a unified framework to achieve complex business objectives through systematic task allocation, state management, and error handling.

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Agent Testing

Agent testing is the process of validating AI agent behavior across prompts, tools, workflows, safety rules, and real-world edge cases before changes reach production.

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Agentic Workflows

Agentic workflows are AI systems that autonomously plan, execute, and adapt across multi-step tasks with minimal human supervision, using patterns like ReAct, Reflexion, and Plan-and-Execute to achieve business objectives.

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AI Agent

An AI agent is an autonomous software system that perceives its environment, makes decisions, and takes actions to achieve specific goals without constant human oversight.

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LLM Context Window: The Complete Guide to Token Limits and Optimization

An LLM context window is the maximum number of tokens a language model can process simultaneously, acting as the model's working memory and constraining what information it can reference when generating responses.

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LLM Integration

LLM integration is the engineering work of connecting language models to your application, data, tools, and guardrails so they can perform useful tasks reliably in production.

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Prompt Engineering

Prompt engineering is the practice of designing, testing, and deploying instructions that guide AI models to generate consistent, reliable outputs in production systems.

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RAG System

A RAG (Retrieval-Augmented Generation) system combines large language models with external knowledge retrieval to generate accurate, domain-specific responses grounded in private data.

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Semantic Search: The Complete Guide to Vector-Based Search

Semantic search uses vector embeddings to find results based on meaning and intent rather than exact keyword matches, enabling natural language queries and understanding of synonyms and context.

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Tool Calling / Function Calling: How AI Agents Execute External Functions

Tool calling (also called function calling) enables Large Language Models to request execution of external functions, APIs, and tools by generating structured JSON requests that your application executes—extending AI beyond text generation to real-world actions.

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Vector Database: The Complete Guide to Semantic Search Storage

A vector database stores high-dimensional embeddings and finds semantically similar data using mathematical distance calculations instead of exact keyword matching.

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