Master the language of AI agents. Explore key concepts, terminology, and VM0-specific features to accelerate your agent development journey.
Showing all terms 29 terms
An autonomous AI program that can understand instructions, make decisions, use tools, and execute tasks independently. Unlike traditional scripts, agents can adapt their approach based on context and iterate toward goals.
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Files, data, or outputs generated by an agent during execution. Artifacts persist across runs and can include code, documents, analysis results, or any other deliverables produced by the agent.
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A markdown configuration file that defines an agent's behavior, personality, goals, and available tools. The AGENTS.md file serves as the agent's blueprint, specifying how it should think, what it can do, and how it should interact with users and systems.
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A concrete operation or task that an agent performs to achieve its goals. Actions can include calling APIs, reading files, processing data, or interacting with external systems. Each action represents a single step in the agent's execution flow.
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A system that intelligently routes agent requests to the most appropriate AI model based on task requirements, cost constraints, and performance needs. The agent router can dynamically select between different LLMs (Claude, GPT, Gemini) or route to specialized models for specific capabilities.
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The information available to an agent during execution, including conversation history, memory, tool outputs, and environmental state. Context helps agents maintain coherence across interactions and make informed decisions.
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A snapshot of an agent's state at a specific point in time, capturing its memory, context, and execution progress. Checkpoints enable reproducibility, allowing you to restore, fork, or replay agent runs.
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A text-based interface for interacting with agents and the VM0 platform. The CLI allows developers to create, deploy, and manage agents through terminal commands, providing powerful scripting and automation capabilities without requiring a graphical interface.
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An agent specifically designed to be invoked and controlled through command-line interfaces. CLI agents can be integrated into scripts, CI/CD pipelines, and terminal workflows, making them ideal for automation tasks and developer tooling.
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Anthropic's official command-line tool for building and deploying AI agents. Claude Code provides a developer-friendly interface for creating agents, managing skills, and integrating with the Claude API. It streamlines the agent development workflow with features like live reloading, debugging tools, and deployment automation.
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A command-line tool for agent development and deployment. Codex provides developers with utilities for building, testing, and managing agents through the terminal. It offers features like code generation, scaffolding, and integration with AI models to streamline the agent development workflow.
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A configuration value that is passed to an agent at runtime, allowing you to customize behavior without changing code. Environment variables are commonly used for API endpoints, feature flags, and non-sensitive configuration settings.
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A packaged container that includes the code, runtime, dependencies, and configuration needed to run an agent. Images provide a consistent and reproducible execution environment, ensuring that agents behave the same way across different deployments.
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The underlying AI model that powers agent reasoning and decision-making. Examples include Claude, GPT-4, and Gemini. Different LLMs have different strengths, costs, and capabilities.
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Information that an agent retains across interactions, enabling it to learn from past experiences and maintain context over time. Memory can include facts, preferences, conversation history, and learned patterns.
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An open protocol developed by Anthropic that enables AI assistants to securely connect to external data sources and tools. MCP provides a standardized way for agents to access resources like databases, APIs, and file systems while maintaining security and context awareness.
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The ability to inspect, monitor, and understand agent behavior through logs, metrics, and execution traces. Observability is essential for debugging, optimizing, and ensuring transparent AI operations.
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The coordination and management of multiple agents, workflows, or tasks to achieve complex goals. Orchestration handles dependencies, sequencing, parallel execution, and resource allocation across distributed agent operations.
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Natural language instructions given to an agent to define its task, behavior, or goal. Prompts can range from simple commands to complex system instructions that shape how an agent thinks and operates.
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The execution environment where agents run, providing the necessary infrastructure, resources, and APIs. The runtime manages agent lifecycle, handles resource allocation, and ensures proper isolation between different agent executions.
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A pre-built integration or capability that agents can use to interact with external services. Skills provide agents with ready-made functionality like accessing Slack, GitHub, Notion, or other APIs without requiring custom implementation.
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A continuous period of agent execution that preserves state, memory, and context. Sessions allow agents to maintain awareness across multiple interactions and resume work without losing progress.
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An isolated execution environment where agents run safely without affecting production systems. Sandboxes provide security boundaries and ensure that agent actions are contained and controlled.
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A markdown configuration file that defines a skill's behavior, inputs, outputs, and implementation logic. The SKILL.md file allows you to create custom skills using natural language and structured metadata, making it easy to extend agent capabilities without writing complex code.
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Sensitive information like API keys, passwords, and tokens that agents need to access external services. Secrets are stored securely and injected into the agent runtime at execution time, preventing exposure in code or configuration files.
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A function or API that an agent can call to perform specific actions. Tools are the building blocks of agent capabilities, enabling them to read files, make HTTP requests, query databases, or interact with any external system.
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A persistent storage space that preserves files and data across agent runs. Volumes provide agents with a file system that persists beyond individual executions, enabling them to store state, cache data, and maintain long-term artifacts.
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An isolated, software-based computing environment that emulates a physical computer. Virtual machines provide complete isolation between agent executions, ensuring security and preventing interference. VM0 uses lightweight virtual machines to run agents with their own operating system, file system, and network stack.
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A sequence of tasks or operations that an agent executes to achieve a goal. Unlike rigid automation scripts, agent workflows can adapt and adjust based on intermediate results and changing conditions.
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