// glossary
Every term you need to understand the world of autonomous AI agents, self-evolving software, and intelligent automation.
An autonomous software system that can perceive its environment, make decisions, use tools, and take actions to achieve goals — without requiring step-by-step human instructions. Unlike chatbots, agents maintain persistent memory, learn from interactions, and improve their own capabilities over time.
A paradigm where AI systems operate autonomously, making decisions and taking actions on behalf of users. Agentic AI goes beyond answering questions — it plans multi-step workflows, uses tools, handles errors, and adapts strategies based on outcomes.
An AI agent that operates independently without constant human oversight. Autonomous agents can monitor systems, detect issues, make decisions, and execute actions 24/7 — escalating to humans only when encountering situations outside their training.
Anthropic's autonomous coding agent built on the Claude language model. Claude Code can read entire codebases, write production-quality code, review pull requests, debug issues, and refactor systems — working alongside human engineers as a tireless programming partner.
A system architecture where an AI agent's actions generate feedback that is used to improve the agent's future behavior — without human intervention. The agent acts, observes outcomes, updates its internal models, and performs better next time. This is the core mechanism behind self-evolving software.
OpenAI's autonomous coding agent that can independently implement features, write tests, fix bugs, and manage development workflows. Codex works in parallel with human engineers, handling implementation tasks while humans focus on architecture and product decisions.
An AI agent specifically designed to write, review, test, and deploy code. Modern coding agents like Claude Code and Codex can understand entire codebases, follow project conventions, and produce production-quality code that passes CI/CD pipelines.
An architectural pattern where each AI agent runs in its own isolated Docker container with independent memory, tools, and configuration. This ensures complete isolation between agents, prevents cross-contamination, and allows independent scaling.
A self-evolving AI agent framework developed by Nous Research. Hermes agents build procedural memory from experience, create and refine their own skills through closed learning loops, and develop an evolving understanding of users over time. The architecture enables agents that genuinely improve with every interaction.
A neural network trained on vast amounts of text data that can understand and generate human language. LLMs like Claude, GPT-4, and Gemini serve as the 'brains' of AI agents — providing reasoning, language understanding, and decision-making capabilities.
An open standard for connecting AI models to external tools and data sources. MCP provides a universal interface so AI agents can interact with any tool — Slack, Gmail, databases, APIs — without custom integration code for each one.
A system where multiple specialized AI agents collaborate to complete complex tasks. Each agent handles a specific domain — one might research, another writes code, another reviews, another deploys. Together they function as an autonomous team.
An approach where different AI models are used for different tasks based on their strengths. Instead of one model for everything, a multi-model system might use Claude for reasoning, GPT-4 for code generation, and an open-source model for cost-sensitive operations.
An open-source AI assistant framework that provides a local-first gateway for AI agents. OpenClaw runs each agent in its own secure container, supports 200+ messaging platforms, and enables multi-model agent deployments with complete data privacy.
In the context of AI agents, procedural memory refers to skills and procedures that an agent creates from its own experience. Unlike static programming, these are learned capabilities that the agent develops through task completion and refines through repeated use.
The practice of designing inputs (prompts) for AI models to elicit desired outputs. While important for basic AI use, advanced agentic systems move beyond manual prompting toward autonomous goal-oriented behavior where the agent determines its own prompting strategies.
A technique that enhances AI model responses by retrieving relevant information from a knowledge base before generating answers. RAG allows agents to access up-to-date, domain-specific information without retraining the underlying model.
Software systems that autonomously improve their own code, fix bugs, optimize performance, and adapt to changing requirements — without manual human intervention. Powered by closed learning loops and coding agents, self-evolving software gets better every day.
A system that can automatically detect failures, diagnose root causes, and apply fixes without human intervention. Self-healing software monitors its own health, triggers automated recovery procedures, and learns from failures to prevent recurrence.
The ability of an AI agent to interact with external tools and services — databases, APIs, file systems, web browsers, communication platforms. Tool use transforms AI from a text generator into an autonomous actor that can take real-world actions.
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