OpenClaw Glossary

Human-in-the-Loop

A design pattern where an AI agent pauses before taking risky actions and waits for a human to approve or reject the action.

MCP (Model Context Protocol)

An open protocol that lets AI agents connect to external tools and data sources through a standardized interface.

Agent Orchestration

The coordination of multiple AI agents working together to complete complex tasks that no single agent could handle alone.

Multi-Agent Systems

Systems where multiple AI agents collaborate, each with specialized roles, to solve problems more effectively than a single agent.

Tool Use

The ability of an AI agent to interact with external tools and APIs — reading data, calling functions, and taking actions in the real world.

Function Calling

A capability of LLMs to generate structured function calls instead of plain text, enabling agents to interact with APIs and tools programmatically.

Agent Memory

The ability of an AI agent to remember information across conversations and sessions, building knowledge over time.

Context Window

The maximum amount of text an LLM can process in a single request — including the conversation history, system prompt, and tool results.

BYOK (Bring Your Own Key)

A model where you provide your own LLM API key (Anthropic, OpenAI, etc.) instead of the platform providing one, giving you full cost control and model choice.

AI Agent Runtime

The execution environment that hosts an AI agent, managing its lifecycle, tool access, memory, and communication with LLM providers.

Agent Gateway

The control plane that routes messages between users and AI agents across multiple channels, managing authentication, rate limiting, and channel-specific protocols.

RAG (Retrieval-Augmented Generation)

A technique where an AI agent retrieves relevant documents or data before generating a response, grounding its answers in real information rather than relying solely on training data.

Local LLM

Running a large language model on your own hardware instead of calling a cloud API, giving you full data privacy and zero per-token costs.

Prompt Injection

An attack where malicious input manipulates an AI agent into ignoring its instructions and performing unintended actions.

AI Guardrails

Safety boundaries that constrain what an AI agent can and cannot do, preventing harmful or unintended actions.

Audit Trail

A chronological record of every action an AI agent takes, providing accountability, compliance evidence, and forensic capability.

Egress Filtering

Network-level control that restricts which external domains an AI agent can communicate with, preventing data exfiltration.

Encrypted Secrets

API keys and credentials stored with AES-256 encryption at rest, only decrypted and injected into the agent at runtime.

Approval Workflow

A process where risky agent actions are paused and routed to a human for review before execution.

Agent Isolation

The separation of AI agents into isolated environments so that one compromised agent cannot affect others.

Zero Trust for AI Agents

A security model where AI agents are never trusted by default — every action must be verified, every tool call audited, and every network request filtered.

AI Agent Sandbox

An isolated execution environment that constrains what an AI agent can access — files, network, processes — preventing it from affecting the host system or other agents.

Kill Switch

An emergency mechanism that immediately stops an AI agent from taking any further actions when triggered by an operator.

Credential Rotation

The practice of periodically replacing API keys and secrets used by an AI agent, limiting the damage window if a key is compromised.

Data Exfiltration

The unauthorized transfer of data from an AI agent to an external destination, typically through prompt injection, malicious tool use, or compromised integrations.

Network Policy

Rules that define which network connections an AI agent can make — inbound and outbound — at the container or cluster level.

Agent Suspension

Temporarily disabling an AI agent so it stops processing messages and executing actions, without destroying its configuration or data.

Policy Engine

A rule system that defines what an AI agent can and cannot do, with versioning, rollback, and enforcement.

RBAC for AI Agents

Role-Based Access Control applied to AI agent management — different team members get different permissions for viewing, configuring, and approving agent actions.

SIEM Export

The ability to export AI agent audit logs to a Security Information and Event Management system for centralized security monitoring.

Docker Sandbox

A Docker container configured with restricted permissions that isolates an AI agent from the host system and other containers.

Tenant Isolation

The complete separation of resources, data, and credentials between different customers (tenants) on a shared platform.

MCP Server

A service that exposes tools and data to an AI agent through the Model Context Protocol, enabling standardized integrations with external systems.

OpenClaw Plugin SDK

A development kit for building custom plugins that extend OpenClaw agent capabilities with new tools, integrations, and behaviors.

OpenClaw Architecture

The technical design of OpenClaw: a gateway that routes messages from 23+ channels to an LLM-powered agent with MCP tool integrations, running in a Docker sandbox.

Agent Gateway Protocol

The communication protocol between the OpenClaw gateway and connected channels, handling message normalization, delivery guarantees, and channel-specific features.

SOUL (System Prompt / Persona)

The system prompt that defines an OpenClaw agent's personality, behavior, knowledge, and constraints — effectively its identity.

Agent Monitoring

Real-time observation of AI agent behavior, performance, and health — including conversation quality, error rates, and resource usage.

Model Routing

Directing different agent tasks to different LLM models based on complexity, cost, or speed requirements.

Production Readiness

The state where an AI agent meets all security, reliability, and operational requirements for serving real users with real data.

Cost Optimization

Strategies for reducing LLM and infrastructure costs when running AI agents without sacrificing quality or reliability.

Agent Deployment

The process of provisioning infrastructure, configuring security, and launching an AI agent into a production environment.

OpenClaw Upgrades

The process of updating an OpenClaw instance to a newer version while maintaining data, configuration, and uptime.

Agent Recovery

Automated detection and correction of agent failures — including container crashes, health check failures, and degraded performance.

Health Checks

Automated probes that verify an AI agent is running, responsive, and functioning correctly at regular intervals.

AI Compliance

Meeting regulatory and organizational requirements for deploying AI agents in production — including audit trails, data handling, and accountability.

AI Governance

The framework of policies, processes, and controls that govern how AI agents are deployed, monitored, and managed in an organization.

SOC 2 for AI Agents

Meeting SOC 2 Trust Service Criteria for AI agent deployments — specifically security, availability, processing integrity, confidentiality, and privacy.

Responsible AI

The practice of deploying AI agents with intentional safeguards for fairness, transparency, accountability, and safety.

HIPAA Compliance for AI Agents

Meeting Health Insurance Portability and Accountability Act requirements when deploying AI agents that handle protected health information (PHI).

GDPR Compliance for AI Agents

Meeting General Data Protection Regulation requirements when deploying AI agents that process personal data of EU residents.

EU AI Act

The European Union's comprehensive regulation on artificial intelligence, establishing risk-based rules for AI systems including transparency, human oversight, and documentation requirements.

AI Agent Insurance

Liability coverage for damages caused by AI agent actions — from incorrect advice to unauthorized transactions to data breaches.

AI Transparency

The requirement to disclose when users are interacting with an AI agent rather than a human, and to make the agent's decision-making process observable.

AI Bias Detection

The process of identifying and measuring unfair or discriminatory patterns in AI agent responses across different user groups.

AI Incident Response

The structured process for detecting, containing, investigating, and recovering from AI agent failures or security incidents.