reasoning effort
Reasoning effort is the amount of internal computational work an AI model applies before producing an answer or action.
AI Terminology
Search clear AI terminology definitions, acronyms, related concepts and the reviewed videos that explain them.
Showing 21–40 of 1130 terms
Clear filtersdata privacy
Data privacy governs how personal, confidential, or sensitive information is collected, used, shared, retained, and protected in AI systems.
agent completion verification
Agent completion verification checks observable evidence that an agent achieved the requested result instead of accepting its claim that the work is finished.
workflow automation
Workflow automation uses software or AI to complete a connected sequence of routine steps with less manual effort.
agent observability
AI agent observability makes an agent's state, actions, tool use, failures, resource use, and outcomes visible enough to understand and operate it.
agent
An AI agent is a system that observes context, decides what to do, and takes actions through tools to pursue a goal.
agent sandbox
An AI agent sandbox is an isolated execution environment that limits which files, processes, networks, credentials, and external systems an agent can access.
cost per completed task
Cost per completed task measures the total AI, tool, infrastructure, retry, and repair expense for each verified useful outcome.
application integration
Application integration connects software systems so data and actions can move through one useful workflow.
context window
A context window is the maximum amount of tokenized information an AI model can consider during one processing session.
front-end code generation
Front-end code generation uses AI to create or modify the user-interface code of websites and applications from instructions or examples.
agent delegation
AI agent delegation assigns a bounded task, context, authority, and expected result from one participant to an agent or another agent.
Model Context Protocol
MCPModel Context Protocol is a standard way for AI applications to connect with external tools and data sources through consistent interfaces.
model selection
Model selection chooses the AI model whose capability, quality, cost, speed, safety, and operating constraints best fit a task.
inference cost
Inference cost is the expense of running a trained AI model to process inputs and produce outputs.
human-AI collaboration
Human-AI collaboration combines human judgment and accountability with AI speed, generation, analysis, and tool use in a shared workflow.
recursive self-improvement
Recursive self-improvement is the proposed process in which an AI system helps improve its own capabilities, then uses those improvements to support further advances.
on-device inference
On-device inference runs an AI model directly on a user's phone, computer or edge device instead of sending each request to a cloud service.
reinforcement learning
RLReinforcement learning trains an AI system to choose actions using feedback about the results of its earlier choices.
stateful agent
A stateful agent preserves task information across multiple events and model calls so its AI behavior can continue coherently over time.