AI Agents Glossary
Autonomous AI systems that reason, plan, and use tools — agentic workflows, multi-agent systems, MCP, and human-in-the-loop patterns.
A2A Protocol (Agent-to-Agent)
A communication standard that enables AI agents built on different frameworks and by different vendors to discover, authenticate, and collaborate with each other. A2A protocol aims to create interoperability across the agent ecosystem.
Agent Authentication
The process of verifying an agent's identity and authorizing its access to tools, data, and services. Agent authentication ensures that only approved agents can perform sensitive actions and that actions are traceable to specific agents.
Agent Benchmarks
Standardized evaluation suites that measure agent capabilities across tasks like web navigation, coding, tool use, and multi-step reasoning. Benchmarks provide comparable metrics for assessing different agent implementations and model versions.
Agent Caching
Storing and reusing the results of previous agent computations, tool calls, or model inferences to reduce latency and cost for repeated or similar requests. Agent caching operates at multiple levels from prompt caching to full response caching.
Agent Cost Optimization
Strategies for reducing the computational and financial cost of running AI agents, including model selection, prompt optimization, caching, and efficient tool use. Cost optimization ensures agent systems remain economically viable at scale.
Agent Debugging
The practice of diagnosing and resolving issues in agent behavior by inspecting reasoning traces, tool call sequences, state transitions, and decision points. Agent debugging requires specialized tools beyond traditional software debugging.
Agent Error Handling
Strategies for detecting, recovering from, and learning from errors that occur during agent execution, including tool failures, reasoning errors, timeout exceptions, and unexpected model outputs.
Agent Evaluation
Systematic methods for measuring agent performance including task completion rate, accuracy, latency, cost, and user satisfaction. Agent evaluation is more complex than model evaluation because it must assess multi-step reasoning and tool use.
Agent Fallback
Backup strategies that activate when an agent's primary approach fails, including alternative models, simpler tool chains, cached responses, or human escalation. Fallbacks ensure continuity of service even during partial system failures.
Agent Guardrails
Safety mechanisms that constrain agent behavior within acceptable boundaries, preventing harmful actions, excessive spending, or unauthorized access. Guardrails operate at the prompt, tool, and system levels to enforce policies.
Agent Handoff
The process of transferring a conversation or task from one agent to another, including relevant context and state. Agent handoffs enable specialized agents to collaborate on complex workflows without losing continuity.
Agent Loop
The core execution cycle of an AI agent where it repeatedly processes input, reasons about next steps, executes actions, and evaluates results until a goal is achieved or a termination condition is met.
Agent Memory
Systems that allow AI agents to store, retrieve, and use information across interactions and sessions. Agent memory encompasses short-term context within a conversation and long-term persistence across separate sessions.
Agent Observability
The practice of instrumenting agent systems to collect, visualize, and alert on operational metrics including latency, cost, error rates, reasoning quality, and task success rates. Observability enables proactive management of agent performance.
Agent Orchestration
The coordination layer that manages how multiple agents, tools, and workflows interact to accomplish complex tasks. Agent orchestration handles routing, state management, error recovery, and resource allocation across the agent system.
Agent Rate Limiting
Controls that restrict how frequently agents can invoke tools, call APIs, or consume resources within specified time windows. Rate limiting prevents agents from overwhelming external services, exceeding budgets, or running away in error loops.
Agent Routing
The process of directing incoming requests to the most appropriate agent based on task type, complexity, user context, or current system load. Agent routing acts as the traffic controller for multi-agent systems.
Agent Safety
The discipline of ensuring AI agents behave predictably, respect boundaries, and do not cause harm through their actions. Agent safety encompasses prompt injection defense, action validation, scope limitation, and impact assessment.
Agent Sandboxing
Isolating agent execution in restricted environments that limit access to system resources, networks, and data. Sandboxing prevents agents from performing unintended or harmful actions beyond their authorized scope.
Agent State Management
The systems and patterns for tracking, persisting, and restoring an agent's working context across steps, sessions, and failures. State management enables agents to handle long-running tasks and recover gracefully from interruptions.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
AutoGen
A Microsoft framework for building multi-agent conversational systems where agents interact through natural language messages. AutoGen supports flexible conversation patterns including group chats, nested conversations, and human participation.
Autonomous Agent
An AI system that independently pursues goals over extended periods, making decisions, using tools, and adapting its approach without continuous human oversight. Autonomous agents operate with broad mandates rather than step-by-step instructions.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.
Code Interpreter
An agent capability that allows the AI to write, execute, and iterate on code in a sandboxed environment. Code interpreters enable agents to perform data analysis, create visualizations, and solve computational problems dynamically.
Computer Use Agent
An AI agent that controls a computer by viewing the screen, moving the mouse, clicking elements, and typing keystrokes, effectively operating software like a human user. Computer use agents interact with any application through the visual interface.
Conversational Agent
An AI agent designed for multi-turn dialogue with users, maintaining context across messages and adapting its behavior based on the conversation flow. Conversational agents combine language understanding with task execution within natural dialogue.
CrewAI
A Python framework for orchestrating multi-agent systems where agents are organized into crews with defined roles, goals, and task assignments. CrewAI emphasizes role-based agent design and sequential or parallel task execution.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Human-in-the-Loop
An agent design pattern where human review or approval is required at critical decision points before the agent proceeds. Human-in-the-loop balances AI automation speed with human judgment for high-stakes actions.
JSON Mode
A model configuration that constrains the output to valid JSON format, ensuring responses are always parseable. JSON mode is a simpler alternative to full structured output when you need valid JSON but do not require strict schema adherence.
LangGraph
A framework built on LangChain for creating stateful, multi-step agent workflows as directed graphs. LangGraph gives developers fine-grained control over agent state, branching logic, and cycle handling.
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Multi-Agent Systems
Architectures where multiple specialized AI agents collaborate to accomplish tasks that exceed the capability of any single agent. Each agent has defined roles, tools, and responsibilities within the system.
Multi-Modal Agent
An AI agent that can process and generate multiple types of content including text, images, audio, video, and code. Multi-modal agents handle tasks that require understanding or producing diverse media formats.
Parallel Tool Calls
A model capability where multiple tool invocations are requested simultaneously in a single response, enabling concurrent execution. Parallel tool calls reduce latency for tasks requiring multiple independent data retrievals or actions.
Planning Agent
An agent specialized in creating structured plans for accomplishing complex goals, including task ordering, resource allocation, and dependency management. Planning agents define what to do before execution begins.
Prompt Chaining
A pattern where the output of one language model call becomes the input for the next, creating a pipeline of specialized prompts that together accomplish a complex task. Prompt chaining offers more control than single-prompt approaches.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Reflection Pattern
An agent technique where the AI evaluates its own output or reasoning, identifies weaknesses or errors, and generates an improved version. Reflection enables self-correction without external feedback.
Retrieval Agent
An agent that specializes in finding and synthesizing information from knowledge bases, databases, documents, and external sources. Retrieval agents combine search strategies with reasoning to locate relevant information for complex queries.
Robotic Process Automation (RPA)
Software that automates repetitive, rule-based tasks by mimicking human interactions with digital systems like clicking, typing, and copying data between applications. AI-enhanced RPA adds intelligence to handle exceptions and unstructured data.
Structured Output
Model responses that conform to a predefined schema such as JSON, XML, or typed objects rather than free-form text. Structured output ensures AI responses can be reliably parsed and consumed by downstream application code.
Task Decomposition
The process where an agent breaks a complex goal into smaller, manageable subtasks that can be executed sequentially or in parallel. Effective task decomposition is fundamental to agent reliability on multi-step problems.
Tool Registry
A centralized catalog of available tools, their schemas, descriptions, and access policies that agents can discover and invoke at runtime. A tool registry decouples tool definitions from agent code, enabling dynamic tool management.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Tree of Thought
An advanced reasoning framework where the model explores multiple solution paths simultaneously, evaluates each branch, and selects the most promising approach. Tree of thought enables more thorough problem-solving than linear chain-of-thought reasoning.
Voice Agent
An AI agent that communicates through spoken language, combining speech recognition, language understanding, reasoning, and speech synthesis to conduct natural voice conversations. Voice agents enable hands-free AI interaction for phone, IoT, and accessibility use cases.
Web Browsing Agent
An AI agent that can navigate websites, extract information, fill forms, and interact with web applications programmatically. Web browsing agents combine language understanding with browser automation to perform research and web-based tasks.