AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
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13,917 terms. Open one for definitions and related concepts.
JSON Mode
A model configuration that constrains the LLM to always output valid JSON, ensuring reliable structured data generation for application integration.
Constrained Decoding
A technique that restricts the tokens a language model can generate at each step, ensuring outputs conform to a specific format, grammar, or schema.
Multi-agent System
A system where multiple AI agents collaborate, compete, or coordinate to accomplish tasks that are too complex for a single agent to handle effectively.
Agent Collaboration
The process of multiple AI agents working together cooperatively, sharing information and coordinating actions to achieve common goals.
Agent Communication
The methods and protocols by which AI agents exchange information, including message passing, shared memory, and conversational interaction.
Agent Delegation
The ability of one AI agent to assign tasks or sub-tasks to another agent, distributing work based on capabilities and availability.
Agent Orchestration
The coordination and management of multiple AI agents, controlling their execution order, communication, and resource allocation to achieve system-level goals.
Supervisor Agent
A central AI agent that coordinates other agents in a multi-agent system, assigning tasks, managing workflow, and synthesizing results.
Worker Agent
A specialized AI agent in a multi-agent system that executes specific tasks assigned by a supervisor, focusing on its area of expertise.
Consensus Mechanism
A method for multiple AI agents to agree on a decision, answer, or course of action when they have different perspectives or conflicting outputs.
Agent Handoff
The process of transferring a conversation or task from one AI agent to another, maintaining context and continuity during the transition.
Agent Routing
The process of directing user requests to the most appropriate AI agent based on the request's topic, intent, complexity, or other classification criteria.
Shared Memory
A common memory store accessible to multiple agents in a multi-agent system, enabling them to share information and maintain consistent state.
Message Passing
A communication pattern where AI agents exchange information through discrete messages, each containing structured data or natural language content.
Agent Memory
The mechanisms by which an AI agent stores, retrieves, and uses information from past interactions to inform its current decisions and maintain continuity.
Short-term Memory
Temporary storage of recent interaction context that helps an AI agent maintain coherence within a conversation or short task sequence.
Long-term Memory
Persistent storage of information that an AI agent retains across conversations and sessions, enabling learning, personalization, and accumulated knowledge.
Working Memory
The active information an agent is currently processing, including the current query, retrieved context, tool results, and reasoning state.
Episodic Memory
Memory of specific past interactions or events, allowing an agent to recall what happened in particular conversations or task executions.
Semantic Memory
An agent's stored general knowledge and facts learned from interactions, organized by meaning rather than by specific events or episodes.
Conversation Memory
The storage and management of conversation history that enables an AI agent to maintain context across multiple turns in a dialogue.
Summary Memory
A memory strategy that condenses conversation history into summaries, preserving key information while reducing the context length needed.
Entity Memory
Memory focused on tracking specific entities mentioned in conversations, maintaining a structured record of what the agent knows about each entity.
Knowledge Graph Memory
An agent memory system that stores information as a knowledge graph of entities and relationships, enabling structured reasoning about connections.
Vector Store Memory
An agent memory system that stores past interactions as vector embeddings, enabling semantic retrieval of relevant memories based on the current context.
Memory Retrieval
The process of finding and returning relevant memories from an agent's memory store, typically using semantic search to match the current context.
Memory Consolidation
The process of organizing, summarizing, and optimizing stored memories over time, merging related memories and discarding redundant information.
Workflow
A defined sequence of steps, decisions, and actions that an AI agent or system follows to accomplish a task, often represented as a graph or pipeline.
Pipeline
A linear sequence of processing steps where the output of each step feeds into the next, commonly used for data processing and RAG implementations.
Chain
A sequence of linked LLM calls or operations where each step's output feeds into the next, used for building complex AI applications from simple components.
Sequential Chain
A chain where each step executes after the previous one completes, with outputs flowing forward through a fixed sequence of operations.
DAG
A Directed Acyclic Graph is a workflow structure where steps have defined dependencies but no cycles, enabling parallel execution of independent steps.
State Machine
A computational model where an agent transitions between defined states based on inputs and conditions, providing predictable and controllable behavior.
Event-driven Workflow
A workflow pattern where processing is triggered by events rather than following a fixed sequence, enabling reactive and asynchronous agent behavior.
Checkpoint
A saved snapshot of an agent's execution state that enables resuming interrupted tasks, time-travel debugging, and human review of agent decisions.
Durable Execution
An execution model where agent workflow state is persisted so that execution can survive crashes, restarts, and interruptions without losing progress.
Orchestration
The coordination of multiple components, services, or agents to accomplish a task, managing execution order, data flow, and error handling across the system.
Tracing
Recording the complete execution path of an AI agent's operations, including LLM calls, tool use, and decisions, for debugging, monitoring, and optimization.
Span
A single operation within a trace, representing one step of agent execution such as an LLM call, tool invocation, or processing step with timing and metadata.
LangSmith
A platform by LangChain for tracing, monitoring, evaluating, and debugging LLM applications, providing observability across the AI application lifecycle.
LangFuse
An open-source observability platform for LLM applications, providing tracing, analytics, evaluation, and prompt management with a self-hostable option.
Arize Phoenix
An open-source observability library for LLM applications, providing tracing, evaluation, and debugging tools with a focus on retrieval and embedding analysis.
Helicone
An open-source observability platform for LLM applications focused on request logging, cost monitoring, and rate limiting with a proxy-based architecture.
Callback
A function that is automatically called when a specific event occurs during agent execution, enabling logging, monitoring, and custom handling of agent operations.
Structured Logging
A logging approach that records events in a consistent, machine-parseable format (typically JSON), enabling efficient analysis and monitoring of AI agent operations.
Cost Tracking
Monitoring and recording the financial costs of AI agent operations, including LLM API calls, embedding generation, tool use, and other billable resources.
Token Tracking
Monitoring the number of input and output tokens consumed by LLM calls, essential for cost calculation, quota management, and usage optimization.
Latency Tracking
Monitoring the time taken by each component of an AI agent's execution, from LLM response time to tool execution and overall interaction duration.
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What is the fastest way to get started?
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