Key terms and concepts in agentic engineering, AI agents, and intelligent automation — explained clearly.
A software library or platform that provides the building blocks for creating AI agents, including abstractions for tool use, memory management, planning, and orchestration. Popular agent frameworks include LangChain, CrewAI, AutoGen, and OpenClaw — each offering different approaches to structuring agent behavior and multi-agent coordination.
The system by which AI agents store and retrieve information across interactions, encompassing short-term working memory (current conversation context), long-term memory (persistent knowledge from past interactions), and episodic memory (specific event recollections). Effective memory systems are critical for agents that need to maintain context across multi-step tasks or long-running relationships.
A category of artificial intelligence systems designed to act autonomously, making decisions and taking actions to achieve goals without requiring step-by-step human instructions. Agentic AI goes beyond passive response generation by proactively planning, using tools, and adapting strategies based on environmental feedback — representing a shift from AI as a tool to AI as a collaborator.
The discipline of designing, building, and deploying autonomous AI agent systems that can reason, plan, and execute complex tasks with minimal human oversight. Agentic engineering combines software engineering, AI/ML, and systems design to create production-grade autonomous workflows.
An autonomous software system powered by a large language model (LLM) that can perceive its environment, make decisions, and take actions to achieve specific goals. Unlike simple chatbots, AI agents can use tools, access external data, maintain context across interactions, and chain multiple reasoning steps together.
An AI system designed to work alongside humans as a real-time assistant, augmenting human capabilities rather than replacing them. AI copilots provide suggestions, automate routine subtasks, surface relevant information, and handle repetitive work — allowing humans to focus on high-level decisions and creative problem-solving.
A phenomenon where an AI model generates content that is factually incorrect, fabricated, or unsupported by its training data or provided context. Hallucinations are a key challenge in deploying AI agents for production use — addressed through techniques like RAG (grounding responses in real data), human-in-the-loop review, confidence scoring, and output validation against authoritative sources.
The process of coordinating multiple AI models, agents, and services to work together on complex tasks. AI orchestration involves managing data flow between components, handling failures and retries, sequencing dependent operations, and ensuring that the overall system produces coherent results — similar to how a conductor coordinates an orchestra.
The field of research and practice focused on ensuring AI systems behave safely, reliably, and in alignment with human values. For agentic systems, AI safety encompasses preventing harmful actions, maintaining human oversight, ensuring predictable behavior, implementing kill switches, and designing systems that fail gracefully — particularly critical as agents gain more autonomy and access to real-world tools.
An AI agent capable of independently completing complex, multi-step tasks with minimal human guidance. Autonomous agents can break down goals into subtasks, execute them in sequence or parallel, handle errors, and adapt their approach based on intermediate results — operating much like a skilled human worker.
Dense numerical representations of text, images, or other data that capture semantic meaning in a high-dimensional vector space. Embeddings allow AI systems to measure similarity between concepts, retrieve relevant information based on meaning rather than keywords, and power semantic search, recommendation systems, and RAG pipelines — forming the mathematical foundation of modern AI understanding.
The process of further training a pre-trained language model on a specific dataset to improve its performance on particular tasks or domains. Fine-tuning adapts a general-purpose model to understand specialized terminology, follow specific output formats, or excel at domain-specific reasoning — without requiring the enormous compute resources needed to train a model from scratch.
A capability of modern LLMs that allows them to generate structured requests to invoke specific functions or APIs. When an LLM determines that it needs to take an action — such as searching a database, calling an API, or performing a calculation — it outputs a structured function call with the appropriate parameters, which the host application then executes and returns results to the model.
Safety mechanisms and constraints built into AI systems to ensure they operate within defined boundaries. Guardrails include input validation, output filtering, content moderation, action restrictions, budget limits, and human approval gates. They are essential for deploying autonomous agents in production — preventing harmful outputs, unauthorized actions, and runaway costs.
A design pattern where AI systems include checkpoints for human review, approval, or intervention at critical decision points. HITL ensures that autonomous agents operate safely by keeping humans involved for high-stakes decisions while letting AI handle routine work independently.
A deep learning model trained on massive amounts of text data that can understand and generate human-like language. LLMs like Claude, GPT, and Gemini serve as the reasoning engine behind AI agents, enabling them to understand instructions, process information, and generate intelligent responses.
An open protocol developed by Anthropic that standardizes how AI applications connect to external data sources and tools. MCP provides a universal interface for AI agents to access files, databases, APIs, and other systems — similar to how USB standardized peripheral connections.
The coordination of multiple AI agents working together on complex tasks, where each agent has specialized capabilities and they collaborate through defined communication protocols. Orchestration ensures agents hand off context, share results, and avoid conflicts while working toward a shared objective.
A branch of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. NLP underpins the ability of AI agents to read documents, understand user requests, extract information from text, generate responses, and engage in natural conversations — making it the foundational technology behind modern conversational AI and agentic systems.
The practice of designing and optimizing the instructions given to large language models to elicit desired behaviors and outputs. Effective prompt engineering is critical for building reliable AI agents — determining how well they reason, follow instructions, use tools, and handle edge cases.
A technique that enhances LLM responses by retrieving relevant information from external knowledge bases before generating an answer. RAG grounds AI responses in real, up-to-date data — reducing hallucinations and enabling agents to answer questions about proprietary or domain-specific content.
A knowledge storage system that allows AI agents to remember and recall information based on meaning rather than exact keywords. Semantic memory uses vector embeddings to store and retrieve contextually relevant information, enabling agents to maintain long-term context across conversations and channels.
An AI agent that maintains persistent state across interactions, remembering previous conversations, user preferences, and task context. Unlike stateless chatbots that treat each message independently, stateful agents build and maintain a model of the ongoing relationship — enabling continuity across sessions, channels, and time.
The ability of an AI agent to interact with external systems, APIs, databases, and software tools to gather information or take actions in the real world. Tool use transforms LLMs from text generators into systems that can search the web, query databases, send emails, write code, and interact with any API.
A specialized database designed to store, index, and query high-dimensional vector embeddings at scale. Vector databases enable fast similarity search — finding the most semantically relevant documents, images, or data points based on meaning rather than exact keyword matches. They are essential infrastructure for RAG systems, semantic memory, and AI-powered search.
AI technology that enables natural, real-time voice conversations between humans and AI agents. Voice AI combines speech-to-text, LLM reasoning, and text-to-speech to create agents that can handle phone calls, conduct interviews, provide support, and engage in complex multi-turn voice dialogues.
The use of technology to automate repetitive business processes and tasks, replacing manual steps with intelligent, rule-based or AI-driven systems. Modern workflow automation goes beyond simple triggers by incorporating AI decision-making, dynamic routing, and adaptive logic.