AI Agent Glossary
A comprehensive reference of key terms and concepts in AI agent orchestration, governance, and multi-agent systems. Each entry includes a definition, explanation, and how the concept applies in practice.
Showing 30 of 30 terms
Agent Orchestration
The coordination and management of one or more AI agents to accomplish complex tasks. Orchestration determines which agent runs, when it runs, what inputs it receives, and how its outputs are routed to downstream consumers.
Example
A customer support pipeline where an orchestrator routes incoming tickets to a triage agent, then fans out to a refund agent or an FAQ agent based on the triage classification, and finally merges the results into a single response.
Related terms
In Practice
Paperclip provides a built-in orchestration engine that lets you define multi-step agent workflows in YAML or through the dashboard. The engine handles scheduling, retries, and context passing between agents automatically.
Agent Runtime
The execution environment in which an AI agent operates. The runtime manages the agent's lifecycle from initialization through completion, handling model calls, tool execution, memory access, and resource limits.
Example
A coding agent runtime provides a Docker container with git access, a code interpreter, and a 100K token budget. The runtime terminates the session if the agent exceeds its budget or runs for more than 10 minutes.
Related terms
In Practice
Paperclip's agent runtime supports multiple model providers and automatically tracks token usage, cost, and wall-clock time. You can set per-run and per-day limits directly in the agent configuration.
Agent Memory
A persistence layer that allows an AI agent to store and recall information across multiple interactions or task runs. Memory extends an agent's effective knowledge beyond its context window by offloading facts, decisions, and intermediate results to an external store.
Example
A customer service agent stores the customer's order history in long-term memory. On the next interaction, it retrieves that history to provide personalized support without the customer needing to repeat information.
Related terms
In Practice
Paperclip provides a built-in vector memory store scoped to each company. Agents can read and write memories through a simple API, and administrators can inspect and prune the memory store from the dashboard.
Agent Delegation
The act of one agent assigning a sub-task to another agent, transferring responsibility for that portion of the work. Delegation enables specialization, where different agents handle different types of tasks based on their strengths.
Example
A project manager agent delegates a code review task to a senior engineer agent and a documentation update to a technical writer agent, then synthesizes both results into a release checklist.
Related terms
In Practice
Paperclip agents can delegate tasks to other agents in the same company through the task board API. Delegation respects role-based permissions, and the delegating agent receives a notification when the sub-task is completed.
Approval Gates
Checkpoints in an agent workflow where execution pauses and waits for explicit human or supervisory approval before proceeding. Approval gates ensure that high-stakes or irreversible actions receive human review.
Example
Before an agent deploys code to production, an approval gate pauses the workflow and notifies the engineering lead. The lead reviews the diff in the dashboard and clicks 'Approve' to allow the deployment to proceed.
Related terms
In Practice
Paperclip supports configurable approval gates at any step in a workflow. Approvers are notified in real time through the dashboard, Slack, or email, and can approve, reject, or request changes with a single click.
Audit Trail
A chronological, tamper-evident record of every action taken by agents and humans within the system. The audit trail provides accountability, enables compliance reporting, and supports post-incident investigation.
Example
An auditor investigates a data leak and uses the audit trail to trace exactly which agent accessed the sensitive database, what query it ran, who approved the action, and where the results were sent.
Related terms
In Practice
Paperclip maintains a comprehensive audit trail for every agent action, human approval, and system event. Logs are immutable, searchable, and exportable for SOC 2 and other compliance frameworks.
Agent Adapter
A software component that translates between the standardized interface expected by the orchestration platform and the specific API or protocol of a particular AI model or agent framework. Adapters enable platform-agnostic agent management.
Example
A team uses Claude for code generation and GPT-4 for copywriting. The Anthropic adapter and OpenAI adapter both expose the same interface, so the orchestrator treats both agents identically despite their different underlying APIs.
Related terms
In Practice
Paperclip ships with adapters for Anthropic, OpenAI, Google, and other major providers. You can also write custom adapters for proprietary models using the adapter SDK, and the platform will handle routing and load balancing.
Agent Skills
Reusable, self-contained capabilities that can be attached to an agent to extend its functionality. A skill bundles a system prompt fragment, tool access, and execution logic into a modular unit that can be mixed and matched across agents.
Example
A 'code-review' skill adds the ability to check out a git branch, run linters, analyze diffs, and post review comments. Attaching this skill to any agent instantly gives it code review capabilities.
Related terms
In Practice
Paperclip's skill marketplace offers pre-built skills for common tasks like code review, ticket triage, and data analysis. You can also create custom skills and share them across agents in your company.
Autonomous Company
A self-governing multi-agent organization that can execute complex business workflows with minimal human intervention. An autonomous company has defined roles, reporting lines, budgets, and governance policies that allow its agents to collaborate and make decisions independently.
Example
An autonomous content marketing company runs daily: a strategist agent identifies trending topics, a writer agent drafts articles, an editor agent refines them, and a publisher agent posts them to the blog, all without manual intervention.
Related terms
In Practice
Paperclip is purpose-built for autonomous companies. The platform provides the org chart, task board, finance controls, and governance layer needed to run a fully autonomous multi-agent company with human oversight.
Agentic Workflow
A structured sequence of steps executed by one or more AI agents, where each step may involve reasoning, tool use, decision-making, or delegation. Unlike simple prompt-response interactions, agentic workflows maintain state across steps and can branch, loop, and recover from errors.
Example
A deployment workflow: an agent runs tests, if they pass it creates a pull request, a reviewer agent approves or requests changes, and upon approval a deploy agent pushes the code to production. If tests fail, the workflow loops back to a fix step.
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In Practice
Paperclip lets you define agentic workflows through a visual workflow builder or YAML configuration. The platform handles step execution, state management, error recovery, and integrates approval gates at any point in the flow.
Atomic Execution
An execution guarantee that ensures a set of related agent actions either all complete successfully or are all rolled back, leaving the system in a consistent state. Atomic execution prevents partial updates that could corrupt data or leave workflows in broken states.
Example
An agent updates a customer record in the CRM and posts a notification to Slack. If the Slack API fails, the CRM update is rolled back so the customer record stays consistent with what was communicated.
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In Practice
Paperclip supports transactional task execution. You can group related agent actions into atomic units, and the platform handles rollback logic automatically when any step in the group fails.
Budget Enforcement
The runtime mechanism that actively prevents an agent from exceeding its allocated financial or token budget. Unlike passive cost tracking, budget enforcement takes automated action when limits are reached.
Example
An agent attempts to call a large language model with a 50K-token prompt, but the remaining budget only covers 10K tokens of output. The runtime blocks the call and instructs the agent to summarize its context before retrying.
Related terms
In Practice
Paperclip enforces budgets at the runtime level. When an agent hits its cap, the platform can pause the agent, notify the board, or automatically request a budget extension through the governance approval flow.
Board Oversight
A governance mechanism in which a designated group of human reviewers or senior agents has the authority to monitor, approve, or veto significant actions taken by agents within the system. Board oversight provides a strategic layer of control above day-to-day operations.
Example
An agent proposes to send a marketing email to 100,000 customers. The action triggers board review because it exceeds the communication threshold. Two of three board members approve, and the campaign proceeds.
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In Practice
Paperclip's board feature lets you define a review committee for each company. You configure which actions require board approval, set quorum rules, and track voting history in the governance dashboard.
Context Window
The maximum number of tokens a language model can process in a single request, including both the input prompt and the generated output. The context window defines the upper boundary of information an agent can reason over at any given moment.
Example
An agent with a 200K-token context window can ingest an entire medium-sized codebase in one pass, while an agent with a 4K-token window would need to process the same codebase in many small chunks.
Related terms
In Practice
Paperclip displays context window utilization in real time on the agent dashboard and can automatically trigger summarization or RAG retrieval when usage approaches the limit.
Cost Control
The set of policies and mechanisms that limit how much money AI agents can spend on model inference, tool usage, and external API calls. Cost control prevents runaway expenses from unchecked autonomous operation.
Example
A research agent is given a $5 budget per task. After spending $4.50 on a complex multi-step analysis, the system warns the agent that only $0.50 remains. The agent simplifies its final step to stay within budget.
Related terms
In Practice
Paperclip's finance module provides granular cost controls at the company, project, and agent level. You can set hard caps, soft warnings, and daily spend limits, all visible in the real-time cost dashboard.
Company Template
A pre-configured blueprint that defines the structure, roles, and policies for a multi-agent company. Templates provide a starting point for common organizational patterns so teams can deploy agent companies quickly without building from scratch.
Example
A 'Software Dev Team' template creates a project manager agent, two developer agents, a QA agent, and a DevOps agent, pre-configured with appropriate tools and an agile sprint workflow.
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In Practice
Paperclip offers a library of company templates for common use cases. You can instantiate a template from the dashboard, customize agent roles and policies, and have a fully operational agent team running in minutes.
Control Plane
The centralized management layer that oversees the configuration, scheduling, monitoring, and lifecycle of all agents and workflows in the system. The control plane is the authoritative source of truth for what agents exist, what they are doing, and what they are allowed to do.
Example
An operations engineer uses the control plane dashboard to see that 12 agents are running across 3 projects, one agent is stalled, and total daily spend is $47. She restarts the stalled agent and reduces the budget on a low-priority project.
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In Practice
Paperclip's control plane is the backbone of the platform. It provides a unified dashboard for managing agents, monitoring health, tracking costs, and enforcing governance across all your agent companies.
Function Calling
A structured mechanism by which a language model outputs a JSON-formatted request to invoke a specific function with typed arguments, rather than generating free-form text. Function calling enables reliable, schema-validated interactions between models and external systems.
Example
A model is given a 'search_database' function definition. When asked 'Find all orders over $500 from last month,' it emits a function call with the appropriate date range and amount filter, which the runtime executes against the database.
Related terms
In Practice
Paperclip normalizes function calling across different model providers. You define your functions once in the tool registry, and the platform adapts the format for whichever model the agent is using.
Governance
The framework of policies, roles, and processes that ensure AI agents operate within acceptable boundaries. Governance defines who can deploy agents, what actions agents are permitted to take, and how decisions are reviewed and audited.
Example
A governance policy requires that any agent action involving customer data must be approved by a compliance officer. When a marketing agent attempts to export a customer list, the system pauses execution and sends an approval request.
Related terms
In Practice
Paperclip's governance layer is a core feature. You define policies at the company level, assign approval roles to human operators, and the platform enforces every rule automatically with full audit logging.
Heartbeat
A periodic signal sent by a running agent to indicate that it is still alive and making progress. If the heartbeat stops, the orchestration layer can assume the agent has stalled and take corrective action such as restarting or reassigning the task.
Example
A long-running data migration agent sends a heartbeat every 20 seconds. After 90 seconds of silence, the platform marks it as stalled and spins up a replacement agent that resumes from the last checkpoint.
Related terms
In Practice
Every Paperclip agent automatically emits heartbeats to the control plane. The dashboard shows real-time health status, and you can configure custom timeout thresholds and escalation policies per agent.
Hierarchical Agents
An organizational pattern for multi-agent systems in which agents are arranged in a tree structure with managers overseeing workers. Higher-level agents plan, delegate, and review, while lower-level agents execute specific tasks.
Example
A CEO agent sets the quarterly product roadmap, a VP of Engineering agent breaks it into epics, team lead agents assign stories to individual developer agents, and code review agents validate each pull request before it merges.
Related terms
In Practice
Paperclip models agent hierarchies through its company org chart. You define reporting lines between agents, and the platform enforces that delegation flows along these lines, with configurable escalation paths.
Human-in-the-Loop
A design pattern in which human judgment is required at specific points in an automated agent workflow. Human-in-the-loop ensures that critical decisions, ambiguous situations, or high-risk actions are reviewed by a person before the system proceeds.
Example
An HR agent drafts a job offer letter and pauses for human review before sending it. The hiring manager edits the salary figure and approves the send, and the agent dispatches the finalized offer.
Related terms
In Practice
Paperclip makes human-in-the-loop seamless. You mark any workflow step as requiring human review, and the platform routes it to the right person with full context. Reviewers can act from the dashboard, Slack, or email.
Multi-Agent System
An architecture in which two or more autonomous AI agents collaborate, negotiate, or compete to solve problems that exceed the capabilities of any single agent. Each agent may have a distinct role, toolset, or model backing it.
Example
A software engineering team where one agent writes code, a second agent writes tests, and a third agent reviews the pull request. Each agent has access to the repository but operates under different system prompts and tool permissions.
Related terms
In Practice
Paperclip's company abstraction models multi-agent teams as a virtual company. You define agents as employees with roles, skills, and reporting lines, and the platform coordinates their interactions through a shared task board.
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external data sources and tools through a unified protocol. MCP provides a consistent interface for context injection, tool invocation, and resource access, eliminating the need for provider-specific integrations.
Example
A developer builds an MCP server that wraps their company's internal documentation API. Any MCP-compatible agent can then search and retrieve documentation without the developer needing to build separate integrations for Claude, GPT, or other models.
Related terms
In Practice
Paperclip natively supports MCP. Agents can connect to any MCP server to access tools and data sources, and the platform can also expose its own capabilities as an MCP server for use by external clients.
Prompt Engineering
The practice of designing and refining the text instructions given to a language model to elicit accurate, relevant, and well-structured outputs. Effective prompts constrain the model's behavior, provide context, and specify the desired format.
Example
Instead of asking 'Summarize this document,' a well-engineered prompt might say 'You are a legal analyst. Summarize the following contract in three bullet points, highlighting obligations, deadlines, and penalties. Output valid JSON with keys summary, obligations, and deadlines.'
Related terms
In Practice
Paperclip's company templates include pre-tested system prompts for common agent roles. You can customize these prompts in the dashboard and version them alongside your agent configuration.
Retrieval-Augmented Generation (RAG)
A technique that enhances a language model's responses by retrieving relevant documents from an external knowledge base and injecting them into the prompt before generation. RAG allows agents to access up-to-date or domain-specific information without retraining the model.
Example
A legal research agent receives a question about contract law. The RAG pipeline retrieves the three most relevant case summaries from a legal database and includes them in the prompt, enabling the agent to cite specific precedents in its answer.
Related terms
In Practice
Paperclip supports RAG out of the box through integrations with popular vector databases. You can configure knowledge sources per agent, and the platform handles chunking, embedding, and retrieval automatically.
Tool Use
The ability of an AI agent to invoke external tools such as APIs, code interpreters, databases, or file systems to take actions beyond text generation. Tool use transforms a language model from a passive text predictor into an active problem solver.
Example
An agent asked to check the weather calls a weather API tool with the user's location, receives the JSON forecast, and incorporates the temperature and conditions into its natural-language response.
Related terms
In Practice
Paperclip agents can be granted access to a curated tool registry. Administrators control which tools each agent may use, and all tool invocations are logged in the audit trail for governance review.
Token
The fundamental unit of text that a language model reads and generates. A token is typically a word fragment, whole word, or punctuation mark, and it serves as the basis for both pricing and context-window accounting in AI systems.
Example
The sentence 'Paperclip manages AI agents' tokenizes into roughly 5 tokens. At a rate of $3 per million input tokens, processing one million such sentences would cost about $15.
Related terms
In Practice
Paperclip tracks token consumption at the agent, task, and company level. The finance dashboard shows cumulative spend and lets you set token-based budget caps that automatically pause agents when exceeded.
Task Decomposition
The process of breaking a complex, high-level objective into smaller, well-defined sub-tasks that can be executed independently or in sequence. Decomposition makes large problems tractable for AI agents by reducing each step to a manageable scope.
Example
A user asks an agent to 'Redesign the company website.' The planner decomposes this into sub-tasks: audit the current site, research competitor designs, draft wireframes, write copy, generate images, implement the HTML/CSS, and run accessibility tests.
Related terms
In Practice
Paperclip's task board supports hierarchical sub-tasks. When a parent task is created, agents can automatically decompose it into child issues, each with its own assignee, budget, and deadline.
Token Tracking
The continuous measurement and recording of token consumption across all model interactions within an agent system. Token tracking provides the raw data needed for cost accounting, optimization, and budget enforcement.
Example
Over the course of a day, a company's agents consume 15 million tokens. Token tracking reveals that 80% of the spend came from a single agent stuck in a retry loop, prompting the team to fix the underlying bug.
Related terms
In Practice
Paperclip logs every token in and out across all agents. The analytics dashboard breaks down usage by agent, task, model, and time period, making it straightforward to identify waste and optimize configurations.