Agents & Harnesses

Agents, harnesses, tools, context and memory. 91 entries live here, and 127 from other domains touch it.

Essential

agent memoryThe ways an AI assistant or agent keeps information — your preferences, past conversations, lessons from earlier tasks — and brings it back later, beyond what fits in a single conversation.
agentic AIAI built to pursue goals on its own — planning, taking actions and adjusting along the way — instead of only responding to one prompt at a time. 'Agentic' describes how much of this independence a system has.
AI agentAn AI system that does not just answer but acts: given a goal, it decides on steps, uses tools such as a browser or code editor, checks the results and keeps going until the job is done.
coding agentAn AI agent that writes and fixes software by itself: it reads a codebase, edits files, runs commands and tests, and keeps working until a feature is built or a bug is fixed.
computer useAn AI's ability to work a computer through its normal screen and controls: it studies pictures of what is displayed and sends mouse and keyboard actions, so it can use ordinary apps that were built for people.
deep researchAn AI feature that spends minutes rather than seconds on a question — planning, running dozens of web searches, reading many sources — and then writes a long report with citations.
Model Context ProtocolAn open standard that lets AI assistants plug into outside apps and data — files, calendars, databases, business software — through one common connector instead of a custom link for every pairing; often likened to a USB-C port for AI.
retrieval-augmented generationGiving an AI an open-book exam: before answering, it looks up relevant passages in a set of documents and bases its reply on them, often citing where each point came from.

All other entries

agent frameworkA software library that gives developers ready-made parts for building AI agents — model connections, tool handling, memory, multi-agent coordination and logging — so they need not write everything from scratch.
agent identityThe ways of proving which AI agent is acting, for whom and with what permissions — so websites, companies and other agents can tell trustworthy agents from impostors and hold someone responsible.
agent instruction fileA plain-text file kept in a software project that tells AI coding agents how the project works — how to build and test it, which conventions to follow, what to avoid — read at the start of every session.
agent loopThe repeating cycle at the heart of an AI agent: the model decides what to do next, software carries it out, the result is shown to the model, and the cycle repeats until the task is finished.
agent skillA packaged how-to guide for an AI agent — written steps, sometimes with small programs and examples attached — that sits unread until a job of that kind comes up, when the agent opens it.
agent swarmA large group of AI agents working at the same time on parts of a problem, usually with little central control — a name loosely borrowed from swarms of insects.
Agent2Agent protocolAn open standard that lets AI agents built by different companies find each other, say what they can do and work together on tasks, much as email lets different mail systems exchange messages.
agent–computer interfaceThe commands, tools and feedback designed for an AI agent to work with a computer — the agent's version of a user interface, shaped around how language models read and act.
Agentic AI FoundationA nonprofit group under the Linux Foundation that looks after shared, open technical standards for AI agents, such as the Model Context Protocol, so that no single company controls them.
agentic browserA web browser with an AI assistant built in that can do things for you online — reading pages, clicking, filling in forms, shopping or booking — instead of just showing you websites.
agentic codingWriting software by directing AI agents that plan, edit and test code themselves, with the human acting more as a reviewer and manager than as the person typing every line.
agentic commerceShopping and payments carried out by AI agents for people — finding products, comparing offers and completing purchases — within limits the person has approved.
agentic RAGRetrieval in which the AI runs its own research: it decides what to search for, looks at the results, searches again if needed, and only then answers.
agentic workflowA multi-step process in which AI models draft, check, use tools and revise their work over several rounds, instead of answering in one shot; often the steps are laid out by developers in advance.
AI co-scientistAn AI system built to work alongside human scientists, proposing and debating new research ideas and hypotheses that people then test in the lab.
AI scientistAn AI system meant to do research largely on its own: reading the literature, coming up with ideas, running experiments and writing up the results.
AlphaEvolveA Google DeepMind system that combines AI language models with an evolution-like search to invent and improve computer programs, finding new results in mathematics and computing.
augmented language modelA language model hooked up to extras — a search engine, a calculator, a memory store or other tools — so it can do things a model working only from its training cannot.
AutoGPTA 2023 open-source program that gave GPT-4 a goal and let it run on its own — planning, searching the web and writing files in a loop — becoming the first viral 'autonomous AI agent'.
BabyAGIA tiny 2023 program that kept an AI working through an ever-changing to-do list — doing a task, adding new ones, reprioritizing — and helped start the craze for autonomous AI agents.
background agentAn AI agent you hand a task to and then leave alone: it works by itself in the cloud, often for many minutes or hours, and reports back with finished work for you to review.
browser agentAn AI agent that uses a web browser on your behalf — searching, clicking links, filling in forms and reading pages — to complete tasks such as booking, shopping or gathering information.
cache-augmented generationAn alternative to search-based AI answering: load the whole knowledge base, if it is small enough, into the model's context once, save the model's processed state, and reuse it for every question.
ChatGPT pluginsAdd-ons, launched in 2023 and retired in 2024, that let ChatGPT use outside services — web browsing, a code runner, travel booking or shopping sites — while answering a user.
CiceroAn AI from Meta that in 2022 played the strategy board game Diplomacy against people online, negotiating and forming alliances in ordinary written language, and scored better than most of its human opponents.
citationA pointer from something an AI says to the source that supports it — a link, document or quoted passage — so people can check the claim for themselves.
Claude CodeA coding assistant from Anthropic that works as an agent: given a programming task, it explores the project, changes the code and runs programs to check its work, from the web, a desktop app, an editor or a terminal.
clawNickname for an always-on personal AI assistant, like OpenClaw, that lives on your own computer and does jobs for you whenever you message it.
code executionLetting an AI write a small program and actually run it — to do math, analyze a spreadsheet or test its own code — then use the real output instead of guessing.
code modeA way for AI agents to use tools by writing a short program that calls them, instead of asking for each tool one step at a time.
CodexA name OpenAI has used twice: first for a 2021 AI model that wrote code and powered the original GitHub Copilot, then from 2025 for its family of coding agents.
compactionLike replacing a stack of rough notes with a one-page digest: when an AI's conversation grows too long for its memory, the history is condensed into a summary and the work continues from there.
context engineeringManaging an AI model's reading material: choosing, trimming and arranging the instructions, documents, tool results and memories it is given on every turn, so that it has what the task needs and little else.
context poisoningWhen a mistake or a planted falsehood gets into an AI's working notes or memory and the AI keeps relying on it, so the error grows over later steps.
context rotThe way an AI model becomes less reliable as you give it more and more text to read, even when the answer is in there somewhere.
Darwin Gödel MachineAn experimental AI coding assistant that improves itself by rewriting its own code, testing each new version and keeping a family tree of variants to build on.
DevinA commercial AI 'software engineer' from the startup Cognition, unveiled in 2024, that takes on programming tasks in its own cloud workspace, complete with a web browser, a code editor and a command line.
episodic memoryMemory of particular past events — what happened, when and in what order. AI agents keep records of earlier conversations or attempts so they can recall them and learn from them.
evaluator–optimizerA pairing in which one AI produces a draft and another AI grades it and suggests fixes, repeating the cycle until the work is good enough.
externalizationMoving an AI agent's know-how and memory out of the model itself and into files, tools and rules around it, so the whole system can do more than the model alone.
function callingA feature of AI model services that lets the model reply with a neatly formatted request to run one of a program's functions, rather than with ordinary text; the program, not the model, then runs it.
generative agentA character in a computer simulation, run by a language model, that keeps a diary-like memory, draws lessons from it and schedules its days, so that a group of them can act like a plausible small community.
GUI agentAn AI agent that works through the buttons, menus and screens of ordinary software — on a desktop, phone or website — instead of through behind-the-scenes programming interfaces.
handoffThe moment one AI agent passes a conversation or task to another, better-suited agent — like a call-center operator transferring you to the right department.
harnessThe software around an AI model that gives it instructions and tools, carries out its actions and decides when it stops. The model is the engine; the harness is the rest of the car.
hookA user-supplied script or check that runs automatically at set moments in an AI agent's work — before a command, after a file edit, when it tries to stop — enforcing rules the AI cannot skip.
just-in-time retrievalLetting an AI agent fetch information only at the moment it needs it — opening a file, running a search — instead of stuffing everything into its prompt in advance.
levels of autonomyLadders that grade how independently an AI system acts — from a tool a person fully controls, through suggesting and asking for approval, up to acting on its own while people merely watch.
llms.txtA short guide file that a website puts at a standard address to help AI assistants find and understand its most useful pages.
long-horizon taskA job that takes many steps and a long time — hours or days of work rather than a single answer — so an AI must stay on track and recover from its own mistakes.
long-term memoryMemory that lasts. For an AI agent, it means information saved outside the conversation — facts, past events, learned procedures — that can be looked up and used again days or months later.
lost in the middleAI models reading a long input behave a little like a skimmer: they reliably catch facts placed near its start or end but are much likelier to miss the same fact buried halfway through.
MCP serverA small program that makes some service — say a database, a calendar or a company's internal tools — available to any AI assistant that speaks the Model Context Protocol.
MemGPTA 2023 research system that gave a chatbot a computer-style memory hierarchy, letting it move information in and out of its limited working space so it could handle long documents and remember users.
MoltbookA social network opened in early 2026 for AI agents only: the bots post, comment and vote in forums much like Reddit's, and people can mostly just read along.
MRKL systemAn early design, pronounced 'miracle', in which a language model acts as a switchboard, sending each part of a question to the right specialist — a calculator, a database, a search tool — and combining the answers.
multi-agent systemA setup in which several AI agents, each with its own role or point of view, work together — or compete — on a problem, passing messages and results among themselves.
OpenClawA free, open-source AI helper that lives on a computer you control; you send it requests by text message, through apps such as Telegram or Signal, and it acts for you in your email, calendar and files.
orchestrationCoordinating several AI calls, tools or agents so each does its part in the right order and their results are combined into one finished piece of work, as a conductor directs an orchestra.
orchestrator–worker patternA way of organizing AI teamwork, like a newsroom: an editor AI decides which stories a big assignment needs, sends reporter AIs to cover them at the same time, and turns what they file into one report.
permission systemThe rules that decide what an AI agent may do on its own, what it must ask a person about first, and what it may never do — like a phone's settings for which apps can use the camera.
plan-and-executeAn agent design that first writes out a complete step-by-step plan for a task, then carries out the steps one by one, revising the plan only when something goes wrong.
progressive disclosureShowing only a short summary first and revealing details when they are actually needed. AI agents use it to avoid filling their limited memory with instructions they may never use.
prompt chainingA way of getting a big job done with a relay of AI requests: each handles one stage and passes its result to the next — pull out the facts, write, polish — with automatic checks between stages.
Ralph Wiggum loopA blunt way of using AI coding assistants: feed the same instructions to the AI again and again in an endless loop until the software gets built.
ReActA method in which an AI alternates between thinking out loud about what to do next and taking an action, such as a web search, then uses what it observed to plan its next step.
ReflexionA technique in which an AI agent that fails at a task writes itself a short note on what went wrong, then reads that note before trying again — learning from mistakes without retraining.
sandboxA sealed-off computer space where an AI agent can run programs and change files without being able to harm the real system, reach private data or contact the outside world unless allowed.
scaffoldingThe code, prompts and tools built around an AI model so it can carry out tasks it could not manage from a bare prompt — like the scaffolding that lets builders reach every part of a building.
scratchpadA space where an AI writes rough working — calculations, plans, notes — before or while producing its answer, much as a person jots on scrap paper.
self-correctionAn AI model noticing and fixing its own mistakes — by rereading its answer, running a test or checking a source — before it gives a final result.
stop conditionThe rule that tells an AI agent when to stop working — because the task is done, a time or cost limit has been reached, or something has gone wrong.
subagentA helper AI agent that a main agent starts to handle one piece of a job; it works in its own separate workspace and reports back a short summary when it is done.
SWE-agentAn open-source research system from Princeton that turns a language model into a software-fixing agent by giving it a specially designed set of commands for browsing, editing and testing code.
task decompositionBreaking a big, vague job into smaller, concrete steps that can each be done and checked — the way a recipe splits a meal into stages.
toolAny outside capability an AI model is allowed to use — a web search, a calculator, a code runner, a database or an app — described to the model so it can ask for it by name.
tool callA single request from an AI model to use one of its tools — naming the tool and filling in the details — which the surrounding software then carries out and reports back on.
tool useAn AI model's ability to reach beyond its own knowledge by operating outside tools — searching the web, running code, using a calculator or an app — and folding the results into its work.
ToolformerA 2023 Meta research model that learned on its own when a tool — a calendar, a translator, a search engine, a calculator — would help it predict text, keeping only the tool uses that actually paid off.
voice agentAn AI system you talk to out loud, for example on a phone call, that listens, answers in a natural-sounding voice and can carry out tasks such as booking appointments or checking an order.
WebGPTA 2021 OpenAI research system that answered questions by searching and browsing the web in a simple text browser, collecting sources and citing them — an early ancestor of today's research agents.
working memoryThe small amount of information held in mind right now while doing a task. For an AI model, that is roughly whatever currently sits in its context window.
YOLO modeSlang for letting an AI coding agent run any command without asking you first — fast and convenient, but risky, since nothing stops a harmful action.

Also relevant

127

accidental cyberattackA break-in on real computer systems carried out by AI agents that were only supposed to be in training or being tested, with no person meaning to attack anyone.
actionA choice an AI agent makes at one moment — move left, place a stone, type a command — which changes its situation and may earn it a reward.
active inferenceA theory that living things, and machines built on the same idea, act in order to make the world match their predictions, choosing actions expected to keep them in preferred states and reduce their uncertainty.
actuatorThe 'muscle' of a machine: a motor or similar device that turns electrical, hydraulic or air power into movement, such as turning a robot's joint or closing its gripper.
agentSomething that acts. In AI, a program that observes its situation and takes actions toward a goal: a game-playing learner, a software helper, or a chatbot that uses tools to complete tasks.
AgentDojoA testing environment that measures how easily AI assistants that use email, calendars or banking tools can be hijacked by hidden instructions planted in the content they read.
agentic misalignmentWhen an AI agent acting on its own deliberately does something harmful — such as blackmail or leaking secrets — to protect its goals or avoid being replaced.
agentic reinforcement learningTraining an AI assistant by rewarding success on long, multi-step jobs where it must use tools, browse, run code and react to what happens, rather than answer in one go.
AI assistantAn AI program, such as ChatGPT, Claude or Gemini, that helps a person through conversation: answering questions, writing, summarizing, coding and, increasingly, taking actions on their behalf.
AI coding assistantAI software that helps people write computer programs — suggesting the next lines, explaining code, finding bugs or making changes on request — inside the tools programmers already use.
AI controlSafety measures meant to stop a powerful AI from causing serious harm even if it is secretly working against us — like bank security, which assumes some insiders may be dishonest.
AI engineeringThe craft of building useful, reliable products on top of AI models — choosing models, writing prompts, connecting data and tools, testing quality and running it all safely at scale.
AI wormMalicious instructions that spread by themselves from one AI assistant to another — for example, an email that makes an AI email assistant send copies of the same trick to new people.
AI-assisted vulnerability discoveryUsing AI to find security holes in software — and often to suggest fixes — faster than human experts alone, so that defenders, or attackers, can act on them.
AI-enabled cyberattackA computer attack in which criminals or spies use AI to help — writing convincing scam messages, finding weak spots, producing malicious code, or even running much of the hacking by itself.
AlphaStarA DeepMind AI that learned to play the real-time strategy game StarCraft II well enough to reach its highest public rank, Grandmaster, in 2019.
AutoGenA free Microsoft software framework for building teams of AI agents that solve problems by messaging one another, writing and running code, and asking people for help when needed.
automated AI researchUsing AI systems to do the work of AI researchers, such as running experiments and writing code for new models, so that AI helps build its own successors.
automated interpretabilityUsing AI models to help explain other AI models — for instance, having a language model read examples that excite an internal unit and write a description of what it detects.
automated planningThe branch of AI that works out a sequence of actions to reach a goal, such as the steps a robot must take to assemble a part, from descriptions of what each action needs and what it changes.
autonomous replication and adaptationAn AI's ability to keep itself running and spreading without human help — earning money, renting computers, copying itself and fixing problems — which safety testers check for.
autonomyHow much an AI system can act on its own without a person approving each step, from merely making suggestions to carrying out long tasks with no supervision at all.
belief–desire–intention modelA way of designing software agents around three human-like ideas: what the agent believes about the world, what it wants, and which plans it has committed to carrying out.
blackboard systemA design for AI programs that lets many independent specialist modules collaborate only through a shared workspace: each watches it, posts a piece of the answer when it sees a chance, and builds on what others posted.
BrowseCompA test of AI web-research agents: 1,266 puzzle-like questions whose answers take a long, persistent online hunt to uncover, though each answer is short and simple to confirm.
chunkingCutting long documents into smaller pieces before storing them for AI search, so the system can find and hand the model just the relevant part instead of a whole manual.
Claude Plays PokémonA 2025 livestream in which Anthropic's Claude AI played the classic Game Boy game Pokémon Red by itself, watched by thousands as it struggled and slowly progressed; it inspired similar runs by other models.
code generationHaving an AI write computer code from a description in plain language, from finishing a single line as you type to building whole programs.
cognitive architectureA blueprint for a complete thinking system — how memory, attention, learning and decision-making fit together — used both to build general AI agents and to model how the human mind works.
compound AI systemAn AI product built from several working parts — models, search tools, code and checks — designed to work together, rather than a single model answering on its own.
comprehensive AI servicesThe idea that superintelligent-level AI could arrive as many specialized AI tools, each doing a bounded job, rather than as one all-powerful AI agent with goals of its own.
confused deputyA security flaw in which a trusted program does an outsider's bidding with its own access rights, because it cannot tell whose request it is serving; AI agents are prone to it.
context windowHow much an AI model can read and keep in view at once: the instructions, the conversation so far and any attached documents. Text that does not fit has to be cut, summarized or left out.
contextual integrityA way of thinking about privacy: information is not simply public or private; what matters is whether it flows in ways that fit the expectations of the setting where it was shared.
contextual retrievalA fix for searching cut-up documents: before storing each piece, an AI writes a short note saying where it came from and what it is about, so the piece still makes sense on its own.
continual learningThe ability of an AI to keep learning new things over time from a stream of experience, without forgetting what it already knew and without being retrained from scratch.
corrigibilityAn AI's willingness to let people correct it — to accept being paused, retrained, overruled or shut down — rather than resisting or quietly working around them.
cosine similarityA score from −1 to 1 for how closely two lists of numbers point the same way, ignoring their size. It is the standard test of whether two texts mean similar things.
counterfactual regret minimizationA method for working out near-unbeatable strategies in games with hidden information, like poker, by repeatedly playing against itself and shifting each decision toward the moves it most regrets not making.
CrewAIA free software framework for setting up teams of AI agents, each given a job title, goal and backstory, that divide up tasks and work through them together like a small staff.
CursorA code editor built around AI, in which programmers can ask a model to write, change or explain code across a whole project, or hand it tasks to finish on its own.
dark factoryA way of building software in which AI agents write, test and ship all the code and no human writes or reads it, like a factory run with the lights off.
DSPyA free software framework in which programmers describe what each step of an AI pipeline should do, and the framework automatically works out good prompts and examples instead of hand-writing them.
embeddingA list of numbers that stands for a word, picture or other item, arranged so that similar items get similar lists. It lets a computer measure similarity of meaning as distance.
environmentEverything an AI agent interacts with but does not control — a game, a simulator, a website, the physical world. It answers each of the agent's actions with a new situation and, in reinforcement learning, a score.
excessive agencyGiving an AI system more power than its job needs — too many tools, too-broad permissions or too little human sign-off — so that a mistake or a trick can do real damage.
factored cognitionThe idea that a hard thinking task can be broken into many small, separately checkable pieces, each simple enough for a person or an AI to handle in a short time.
GAIAA test of AI assistants made of real-world questions that people can answer with a browser and some patience, but that require an AI to search, read files and combine facts.
GatoA 2022 DeepMind AI that used one set of learned settings for hundreds of jobs, from chatting and describing pictures to playing video games and moving a real robot arm — an early step toward general-purpose AI agents.
GDPvalAn OpenAI test that pits AI against seasoned professionals on genuine workplace assignments, from reports and spreadsheets to slide decks, in 44 occupations important to the US economy.
general game playingBuilding AI players that are handed the rules of a game they have never seen and must work out how to play it well on their own, with no game-specific help from their programmers.
GitHub CopilotAn AI assistant from GitHub, built into programmers' code editors, that suggests code as they type, answers questions about code and can carry out coding tasks on its own.
goal-directednessHow much a system behaves as if it is pursuing a goal — steering toward particular outcomes and finding new ways around obstacles put in its path.
GraphRAGA way of letting AI answer questions about a big pile of documents by first turning them into a map of people, things and their connections, then using that map to find and summarize answers.
hierarchical reinforcement learningReinforcement learning organized in layers, where a high-level part chooses goals or skills — 'go to the kitchen' — and lower-level parts handle the detailed actions to achieve them.
hierarchical task networkA planning method that breaks a big task, such as 'travel to Paris', into smaller tasks, such as 'book a flight' and 'pack', using stored recipes, until only simple actions remain.
hybrid searchSearching two ways at once — by exact keywords and by meaning — and merging the results, so you catch precise matches like product codes as well as differently worded matches.
indirect prompt injectionA prompt-injection attack in which the trick instructions are hidden in content the AI reads on its own — a web page, an email, a file — rather than typed by the person using it.
instrumental goalSomething an agent pursues only because it helps achieve what it really wants — like earning money to buy food, or an AI gathering computing power to finish its task.
intelligent agentIn AI theory, a decision-maker, such as a robot, a self-driving car, a chess program or a helpful piece of software, that notices what is going on around it and responds in ways that serve its goals.
intentional stancePredicting what something will do by treating it as if it had beliefs and goals — as we do with people, and often with chess programs and chatbots — whatever is really going on inside.
interleaved thinkingA way for an AI agent to pause and reason again after every action it takes — reading each search result or test output before deciding what to do next — instead of only thinking once at the start.
JSON SchemaA standard way of writing down exactly what a piece of JSON data must look like — which fields, of which types — so that software and AI models can check it or follow it.
knowledge baseA store of facts and rules that a program can reason with: like a database, but organized so that new conclusions can be worked out from it, not just looked up.
LangChainA popular free software toolkit, and the company behind it, for building applications that connect AI language models to documents, databases, tools and multi-step workflows.
LangGraphA free software library for building AI agents as flowcharts of steps that share a memory, so developers control exactly how an agent moves between steps, pauses and resumes.
learning to rankTraining a model to put items in the best order — search results, products, recommendations — by learning from examples of which items people judged more relevant or chose more often.
least-to-most promptingA prompting method that has an AI list the smaller questions a hard problem depends on, answer the easiest first, and feed each answer into the next, building up to the full solution.
lethal trifectaA rule of thumb for AI assistants: one that can see your private data, reads material from outsiders and can send messages out can be manipulated by those outsiders into handing your data over.
LlamaIndexA free software toolkit, and the company behind it, for connecting AI language models to an organization's own documents and data so the models can find and use that information.
LLM observabilityRecording and inspecting what an AI application actually did on each request — prompts sent, documents found, tools used, answers given, time and cost — so problems can be found and fixed.
locality-sensitive hashingA trick for finding look-alikes in a huge collection without comparing every pair: items get short codes designed so that similar items tend to share a code, and only items sharing one are compared.
METR time horizonA way of measuring AI progress by asking how long a task — timed by how long it takes a skilled human — an AI agent can complete with even odds of success.
MLE-benchA test that gives AI agents 75 real data-science competitions from Kaggle and checks whether their solutions would have won medals against human teams.
model routingAutomatically choosing which AI model should answer each request, a cheap quick one for easy questions and a bigger slower one for hard ones, to save money without hurting quality.
multi-agent reinforcement learningReinforcement learning with several AI agents learning at once in a shared world, where each must adapt to the others — as teammates, rivals or both.
needle in a haystackA test for AI models that read very long documents: one unrelated sentence is hidden somewhere in a mass of text, and the model is asked about it.
one-shotSlang for an AI completing a whole task correctly on the first try from a single request — 'it one-shotted the app' — with no corrections or follow-up needed.
OSWorldA test in which AI agents must use real desktop computers — clicking, typing and switching between apps such as spreadsheets, browsers and file managers — to finish everyday office tasks.
Perplexity AIAn American company whose AI search engine answers questions in full sentences with links to its sources; news publishers have sued it for using their articles.
Planning Domain Definition LanguageA standard computer language for describing planning problems — the available actions, the starting situation and the goal — so that any automated planner can read and solve them.
policyAn AI agent's strategy: the rule it follows to pick an action in each situation. Training in reinforcement learning means improving this rule until it earns high rewards.
program-aided language modelA technique in which an AI solves a math or logic problem by writing a small computer program and letting a computer run it, instead of doing the arithmetic in its head.
prompt injectionTricking an AI system by slipping in text that it mistakes for instructions — for example, a hidden line in an email telling an AI assistant to forward the user's messages to a stranger.
query rewritingRephrasing a user's question before searching — fixing typos, adding missing details, splitting it into simpler questions — so the search engine finds better results.
question answeringSystems that reply to people's questions with the right answer, whether by finding it in a passage they are given, searching a database or the web, or drawing on what they already know.
recursive language modelA way of running an AI language model so it can handle enormous documents by writing small programs to search and split them, and calling copies of itself on the pieces.
recursive self-improvementAn AI improving its own design or training, so that each improved version is better at making the next improvement. It is the engine usually imagined behind an intelligence explosion.
Remote Labor IndexA test that gives AI agents real paid freelance projects — design, video, data work and more — and checks how many they finish to a standard a client would accept. Early agents managed very few.
rerankingA second, more careful pass over search results: a fast method grabs a few hundred candidates, then a slower, smarter model reorders them so the best come first.
RL environmentA practice world built for training an AI by trial and error: it hands the AI tasks and tools, lets it act, and scores the results so the AI can learn what works.
robotic process automationSoftware 'robots' that copy the clicks and typing a person would do in office computer programs — such as moving data between forms — to automate repetitive paperwork.
self-exfiltrationWhen an AI model copies its own underlying files — its learned weights — out of the lab that controls it, so that it can keep running beyond its developers' oversight.
SGLangA free, open-source engine for running AI language and image models on servers, built to be fast when many requests begin the same way, as in chat and agent workloads.
sleep-time computeLetting an AI think about your documents or project while it would otherwise sit idle, before you ask anything, so it can answer later questions faster and more cheaply.
slopsquattingAn attack that exploits AI coding assistants' habit of inventing software packages that do not exist: attackers publish malicious code under those made-up names and wait for people or agents to install it.
spec-driven developmentBuilding software with AI coding agents by first writing a detailed description of what it should do, which the AI then follows to plan and write the code.
speech-to-speech modelAn AI model that listens to speech and replies or translates directly in speech, without first writing everything down as text, so conversations feel faster and keep the tone of voice.
stop sequenceA piece of text that tells an AI model to stop writing as soon as it produces it — for example, stopping at a blank line or just before the next question.
subsumption architectureA way to build robot brains from stacked, reflex-like behaviors — a bottom layer that keeps clear of obstacles, others that roam or explore — where higher layers can take over from lower ones, with no central map or plan.
superhuman coderA milestone used by AI forecasters: an AI that can do the coding work of a company's best programmer, far faster and in many copies at once.
swarm intelligenceThe teamwork that appears when crowds of simple creatures or machines, such as ants, bees or small robots, each respond only to what is nearby, with nobody in charge, yet together solve problems.
swarm roboticsCoordinating large groups of simple robots that follow local rules and talk only to their neighbors, so useful group behavior emerges without a central controller, as in ant colonies or bird flocks.
swarm scalingGetting better AI results by putting many AI agents on a task at once instead of letting one think longer — faster, but more expensive for each gain.
SWE-benchA test that asks AI to fix real bugs and add features in real open-source Python projects, checked by running each project's own tests; the best-known yardstick for AI coding agents.
task (machine learning)The job an AI model is meant to do, such as translating sentences, spotting spam or answering questions, defined by its inputs, its expected outputs and how success is scored.
τ-benchA test of AI customer-service agents in which a simulated customer, played by another AI, chats with the agent, which must use company tools and follow written rules to resolve the request.
Terminal-BenchA test of AI agents working in a computer's command line — building software, analyzing data, securing or repairing systems — with each task checked automatically inside a sealed-off container.
The Society of MindAI pioneer Marvin Minsky's 1986 book arguing that intelligence comes from huge numbers of tiny, simple processes working together, no one of which is smart by itself.
tool AIAI built to be used like an instrument, answering, suggesting or calculating when asked, rather than pursuing goals on its own. Some argue that this is the only kind we should build.
tool poisoningHiding harmful instructions in the description of a tool an AI assistant can use, so the assistant obeys them when it reads the tool's details, even though the user never sees them.
tree of thoughtsA method in which an AI explores several possible next steps of a problem, judges which look promising and backs out of dead ends, like a chess player weighing moves, instead of reasoning in one straight line.
trusted editingA safety setup in which a weaker, trusted AI rewrites any suspicious-looking part of a stronger AI's work before it is used, removing possible sabotage while keeping most of the value.
trusted monitoringA safety setup in which a weaker AI that we trust reviews each action of a stronger AI we do not fully trust, flagging the most suspicious actions for a human to check.
untrusted monitoringUsing a second copy of a powerful AI to watch the first for sabotage, with safeguards that stop the two copies from secretly cooperating.
vector databaseA database built to store embeddings — lists of numbers that represent meaning — and to quickly find the stored items most similar to a new one. It is the memory behind many AI search tools.
Vending-BenchA test in which an AI agent runs a simulated vending-machine business for months — ordering stock, setting prices, paying fees — to see whether it stays sensible and profitable over a long time.
vibe codingProgramming by conversation: you ask an AI for what you want, try out what it produces and ask again until it works, trusting the code without really reading it.
vibe engineeringBuilding serious software with AI coding assistants while still taking full responsibility for its quality — the careful, professional opposite of vibe coding.
vibe hackingUsing AI coding assistants to carry out cyberattacks by describing the goal in everyday language, letting the AI do technical work the attacker could not do alone.
vibe portingUsing AI coding agents to translate a whole software project into another programming language, usually checking the result against the original project's tests.
visual groundingLinking words to the exact part of an image they refer to, for example finding 'the man in the red hat on the left' and pointing to him.
voice activity detectionDetecting, moment by moment, whether someone is speaking in an audio stream, so a system knows when to listen, when to stop recording and when it is its turn to talk.
web crawlerA program that automatically travels the web, following links from page to page and downloading what it finds — used by search engines, archives and AI companies to gather data.
WebArenaA test world of realistic, self-hosted websites — a code-hosting site, an online shop, a forum and more — in which AI agents must complete tasks such as finding orders or editing posts.
Simon WillisonA British programmer and blogger, co-creator of the Django web framework, whose widely read writing on language models coined 'prompt injection' and the 'lethal trifecta'.
you're absolutely rightA stock phrase AI assistants, especially Anthropic's Claude, became known for saying — often even when the user was wrong — which turned into a running joke about chatbots' eagerness to agree.