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.
Agent Evaluation
Agent evaluation is the practice of measuring how well an AI agent completes real tasks, using repeatable test cases, graders, and metrics.
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.
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.
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.
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.
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.
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.
AI Audit Trail
An AI audit trail is a tamper-resistant record of what an AI system or agent received, decided, and did, kept so people can review, investigate, and prove what happened.
AI Guardrails
AI guardrails are controls placed around an AI model or agent that check inputs, outputs, and actions against defined rules before anything reaches a user or a system.
Data Sovereignty
Data sovereignty is the principle that data is subject to the laws of the country where it is collected or stored, and that its owner keeps control over where it goes and who can access it.
Durable Execution
Durable execution is a way of running code so that its progress is saved, which lets a long-running process survive crashes, restarts, and outages and resume where it stopped.
LLM as a Judge
LLM as a judge is a method where one language model scores or compares the outputs of another model or agent against criteria you define.
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.
LLM Hallucination
An LLM hallucination is a confident, fluent answer from a language model that is false or not supported by any source.
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.
MCP Server
An MCP server is a program that exposes tools, data, and prompts to AI applications using the Model Context Protocol.
Model Context Protocol (MCP)
The Model Context Protocol is an open standard that lets AI applications connect to tools, data sources, and prompts through one common interface.
Pilot Purgatory
Pilot purgatory is the state where an AI pilot keeps running, or keeps being restarted, without ever reaching production or proving business value.
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.
Prompt Injection
Prompt injection is an attack where malicious text in an input makes an AI model ignore its instructions and take actions its owner did not intend.
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.
Shadow AI
Shadow AI is the use of AI tools, models, or agents inside an organization without approval, review, or oversight from IT, security, or compliance.
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.
Venture Studio
A venture studio is an organization that repeatedly creates and co-founds new companies, supplying talent, capital, and operating support in exchange for an ongoing equity stake.
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