What is action space?
The set of actions an agent is allowed to choose from at a given step.
Glossary · 157 sourced terms
An AI agent glossary explains the words used for systems that plan, use tools, remember context, work together, and act within limits. Each definition sits beside its source and the day we checked it. Related entries help you follow the idea without losing your place.
The set of actions an agent is allowed to choose from at a given step.
A search method where the system chooses what to find, checks it, and may search again before it answers.
The power to choose goal-led actions within a given setting.
A file that tells other systems who an agent is, what it can do, and how to reach it.
The process by which a user or another system finds an agent and learns what it can do.
A test of an agent’s whole run: its choices, tool calls, errors, and final result.
Rules, controls, and records that keep agents answerable in real work.
The names, traits, and proof that let a system tell one agent from another.
The repeated cycle in which an agent observes, decides, acts, checks the result, and chooses what to do next.
A file that lists an agent’s purpose, tools, limits, inputs, outputs, and rules.
Ways for agents to ask for, approve, send, or receive money within set limits.
Shared rules that let agents trade tasks, messages, identity, and results.
A list of agents, what they can do, and where other systems can reach them.
The work of keeping an agent’s goals, choices, and effects within set limits.
Protection for agents, tools, data, keys, and users against attack or misuse.
A reusable set of instructions and tools that gives an agent a named ability.
A group of agents that often coordinate through local rules or shared signals.
Two or more agents with assigned roles that cooperate on one goal.
A defined sequence of agent decisions, tool calls, checks, and handoffs used to complete a task.
Able to seek results on its own, especially when used of AI.
AI designed to choose and carry out actions toward a goal, often through one or more agents.
Trade in which agents help find, buy, sell, or pay for goods under set rules.
RAG in which an agent plans and changes its searches to meet a goal.
A system that observes a setting, chooses goal-led actions, and acts on that setting.
A point where an agent must stop and get consent before it takes a high-risk step.
A machine system that predicts or makes choices for goals set by people.
A signed or recorded statement that a required action, check, or condition was completed.
Tool use in which the agent chooses when, why, and how to call a tool without approval for each call.
A check that the system initiates and runs on its own before accepting or advancing work.
The degree to which an agent can choose and carry out actions without new human direction.
An agent that works without an open user session. A clock or event may start it.
The fixed starting process that an agent can follow, extend, or adapt while doing a task.
A design in which several agents or modules read from and write to one shared working state.
The process of finding what tools, skills, services, or agents are available for a task.
A security model where holding a hard-to-forge token grants the rights it names.
Text or tokens a model produces as steps before an answer. They may not show its true internal reasoning.
A control that stops an agent after a risk alert, repeat failure, cost limit, or unsafe state.
The practice of tying a factual statement to evidence that can be checked.
A mistake that changes later choices, causing the size or cost of the error to grow across a run.
A failure where someone tricks a trusted system into misusing its access.
Saving reused prompt or context material so later model calls can be faster or cheaper.
Making working context smaller while keeping the facts and links a task needs.
The craft of choosing what a model sees, when it sees it, and in what form.
The instructions, facts, state, rights, and history given to one agent step.
The token limit shared by a request’s input and generated output.
Permission granted to an agent to act for someone else within a stated scope and time.
Giving a task or limited power to another person or agent.
A model writes a plan without first searching through many other paths.
Work that can survive a restart, wait for an event, and resume from saved state.
A system that selects or changes the model it uses according to the task, state, cost, or risk.
A real or simulated world where an agent acts through a body, robot, or set of sensors.
The exchange in which an agent observes a world, acts on it, and receives new information in return.
Memory of past events or runs: what happened, when, and under what conditions.
Finding a failure, returning to a safe state, and choosing the next useful step.
Sending a choice or problem to a person or system with more power or skill.
A structured test that measures a system against stated criteria and evidence.
A person, model, rule, or program that scores an output or run against set tests.
An agent that starts or changes work when a declared event occurs.
A risk in which an agent has more functions, permissions, or freedom than its task needs.
A way to improve later choices by using signals from past results.
A workflow that collects results or judgments and uses them to improve later behavior.
The maximum age evidence may reach before it must be checked again.
A model feature that names a function and gives the inputs for software to run it.
The range of tasks and settings in which a system can work without being rebuilt for each one.
How well a system keeps its choices aimed at a stated result.
A method that maps ideas as a graph so they can branch, link, and change.
The control of agent work as nodes and transitions in a graph rather than one straight sequence.
A rule or check that blocks, changes, or escalates an input, output, or action when a condition is met.
Passing control, context, or duty from one agent to another.
Search guided by an estimate of which options are most likely to lead to a good result.
A design in which a person must take part in selected decisions or stages of the work.
A design in which an agent acts on its own while a person monitors and can intervene.
A property that lets the same action run again without causing a second change.
An attack where bad instructions hide in data an agent reads, such as a page, file, email, or tool result.
When unlike systems can share data and work together.
A signal that pauses or stops an agent so its state can be inspected, changed, or resumed.
A model used to score or compare another model’s output or behavior.
An AI model trained on vast amounts of text to understand and generate language.
The rule that a user, agent, or service should receive only the access needed for its current task.
The use of a language model to grade, rank, or critique outputs under a stated rubric.
An agent role shaped mainly by instructions about its skills, duties, and limits.
An agent designed to continue across long waits, many steps, or more than one user session.
Information kept across runs or sessions so an agent can use it later.
A program that gives AI apps tools, files, or prompts through MCP.
Information or state an agent keeps or finds again after its current reply.
Turning many short-lived records into a smaller set that lasts.
An attack that plants false or harmful facts in memory to sway later actions.
The selection of stored information for use in the agent’s current context.
A database or service where an agent keeps facts for later use.
A name used by the community around OpenClaw.
A plan search that samples possible outcomes and spends more effort on promising paths.
A system in which two or more agents interact, cooperate, compete, or divide work.
A setting where an agent mainly sees and acts through written or spoken words.
A digital ID for software, an agent, a workload, or a device rather than a person.
The ability to understand a system from its traces, logs, measures, state, and other clues.
Information an agent gets from its setting before or after it acts.
The set of signals an agent may get from its setting.
The logic that decides which agent, tool, or workflow runs, in what order, with what state, and when it stops.
The chance that at least one of k attempted outputs passes a test.
Tool use in which software or a fixed workflow decides when the tool runs rather than the model deciding.
A check that observes or scores work without controlling what happens next.
A cycle where an agent sees its setting, takes an action, and checks the result.
An agent pattern that first creates a plan and then carries out its steps, revising when needed.
The part of an agent system that proposes goals, steps, dependencies, or action sequences.
An agent design that splits deciding what to do from doing it.
The process of choosing a sequence of actions intended to reach a goal.
Making a plan by testing and comparing paths before choosing one.
A workflow in which the system creates, checks, and carries out a plan.
Stored knowledge of how to perform a task, such as rules, routines, or reusable skills.
An attack that puts commands in model input to override the task or rules.
A claim shown with a warning because its evidence is not yet strong enough.
Tool use that finds outside facts and puts them into the model’s context.
An agent pattern that puts thought, action, and observation steps in a loop.
The deliberate search for ways a system can fail, be misused, or be attacked.
A step in which an agent reviews earlier work and uses the review to change what it does next.
A method that finds outside facts and gives them to a model before it writes.
An agent set up for a clear job, duty, or field of work.
An agent that decides which specialist, model, tool, or workflow should receive a request.
A setting whose state or replies change through explicit rules.
A closed setting that limits what code or an agent can reach and change.
A graph of possible states or ideas, linked by available moves or known ties.
The order and method used to visit options in a search tree or graph.
A branching map of choices and the states that may follow them.
Stealing passwords, keys, private data, or other guarded facts from a system.
A model’s review of its own work against clear rules.
A loop where a model reviews and rewrites its own output.
Memory of facts, ideas, and links without the full event where they were learned.
An account that lets software or a workload prove its identity and gain access.
Information that more than one agent or component can read or change during a task.
State kept for the current run or conversation, such as messages, tool results, plans, and checkpoints.
Testing actions in a model or mock world before using them in the real one.
A list of reusable skills, what they do, how to call them, and what they need.
A record of where information came from and how it was collected or changed.
An agent that assigns work, watches other agents, and chooses when to retry, raise an issue, or stop.
The world, tools, rules, and feedback within which an agent attempts a task.
A defined measure of whether an agent achieved the intended outcome.
A rule that tells an agent loop when to stop.
The process by which a model or agent requests that software run a named tool with stated arguments.
Finding tools and learning their names, inputs, outputs, and limits.
A failure returned when a tool cannot finish a request as asked.
One specific request to run a tool with a set of arguments.
An attack that alters a tool, its description, or its output so an agent is misled or compromised.
A directory that lists tools and the information needed to discover and call them.
The data or error returned after a tool invocation.
The logic that sends a request to the most suitable tool or tool provider.
A structured description of a tool’s name, purpose, inputs, and output shape.
The decision about which available tool, if any, should be used for the current step.
The use of an outside tool for its result or for the change it causes.
A workflow organized around selecting, calling, and checking one or more tools.
The ordered record of an agent’s states, actions, inputs, and outputs during a run.
A method that explores several branches of thought and compares their value.
A broad workflow made to handle many task types through steps and tools.
Work that leaves enough evidence for another party to check what ran and which rules it followed.
The rules and expected behavior that govern how an agent browses, collects, and acts on the web.
An agent that does tasks assigned by a lead agent or system.
Goals, facts, and partial results kept close while an agent works.
A map an agent uses to predict how its world may change after an action.