AI Knowledge Base 2026

AI Glossary 2026

Clear definitions for the era of Agentic AI and Spatial Intelligence.

LangGraph

A framework by LangChain for creating stateful multi-actor applications with LLMs, modeling agents as graphs.

Large Language Model (LLM)

A Large Language Model (LLM) is a neural network with billions of parameters trained on vast amounts of text data to understand and generate human language. LLMs form the foundation of modern AI applications — from chatbots and code assistants to complex analytical tools. The architecture is based on the Transformer model, introduced by Google Research in 2017. Through self-attention mechanisms, LLMs can capture relationships across long text passages and generate context-aware responses. Well-known examples include GPT-4 from OpenAI, Claude from Anthropic, and Gemini from Google. The training process involves two main phases: pre-training on large, unstructured datasets (books, web pages, code) followed by fine-tuning for specific tasks. Techniques like Reinforcement Learning from Human Feedback (RLHF) further improve output quality and safety. For businesses, LLMs matter because they can automate tasks that previously required human language competence: content creation, summarization, translation, code generation, and data analysis. Choosing the right model depends on factors like context window size, latency, cost, and data privacy requirements. An important distinction: LLMs are probabilistic systems. They generate statistically likely text continuations, not factually verified statements. This makes strategies like Retrieval Augmented Generation (RAG) and robust evaluation processes essential for production use.

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Inference & Engineering

LLM Orchestration

LLM Orchestration refers to the coordinated management and control of multiple large language models (LLMs) within an AI system. It involves selecting different models for specific tasks, sequencing or parallelizing their execution, and intelligently combining their outputs. Orchestration also includes managing model switches based on cost, latency, or specialization, handling fallbacks during model failures, and maintaining context across different model calls. Modern LLM orchestration platforms enable developers to build complex AI workflows that leverage different models for reasoning, code generation, translation, or specialized domain expertise while ensuring consistent quality and performance.

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Inference & Engineering

LLMOps

LLMOps (Large Language Model Operations) is a set of practices and tools for managing the entire lifecycle of large language models, from development and training to deployment, monitoring, and maintenance. It focuses on streamlining the process of building and deploying LLMs in production environments.

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Reasoning & Reliability

Local LLMs

Local LLMs are large language models that run entirely on local hardware without requiring cloud connectivity. They provide privacy, reduced latency, and offline capabilities, making AI accessible in environments with connectivity or data sovereignty constraints.

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Agentic Infrastructure

Long Context Window

A long context window refers to the capability of a large language model (LLM) to process very large amounts of text within a single session. While early language models could only handle a few thousand tokens at a time — typically 4,000 to 8,000 — modern models such as Gemini 1.5 Pro, Claude 3.5 Sonnet, and GPT-4o now support context windows ranging from 128,000 up to one million tokens. The practical implications are significant: a long context window enables the analysis of entire codebases, extensive legal contracts, multi-hour transcripts, or complete company handbooks within a single AI query — without the need to split content into smaller chunks. This reduces implementation complexity, prevents information loss from chunking, and produces more coherent outputs across long documents. However, large context windows come with trade-offs. Models can suffer from the lost-in-the-middle effect, where information in the middle of a long context is processed less accurately than content at the beginning or end. Latency and inference costs also increase substantially with context length — a critical factor in system architecture decisions. For enterprises working with extensive documentation, knowledge bases, or complex multi-step workflows, long context windows are a decisive performance parameter when selecting the right AI model for a given use case.

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Agentic Business

Long-Horizon Agent

A long-horizon agent is an autonomous software system capable of planning, executing, and monitoring complex, multi-step tasks over extended periods—ranging from several hours to days or even weeks—without human intervention. Unlike traditional, reactive AI assistants that operate on single-turn prompt-response cycles, long-horizon agents are strictly goal-oriented. They break down a high-level objective into sequential sub-tasks, maintain internal state, manage dynamic context, and interact with external developer tools, execution sandboxes, or APIs. The core challenge and defining characteristic of long-horizon execution is self-healing error recovery. If the agent encounters a bug, API timeout, or unexpected environment state during a middle step, it does not abort the task. Instead, it analyzes the failure log, refines its execution path, and retries with a modified strategy. Achieving this level of autonomy requires robust orchestration architectures, state-tracking loops, and context budgeting policies to prevent the accumulation of token costs over long runtime cycles. In enterprise settings, long-horizon agents are prominently deployed in autonomous software engineering (e.g., resolving complex codebase issues evaluated on benchmarks like SWE-bench), deep market research, and multi-system business process automation. They represent the transition from simple chatbot widgets to digital coworkers capable of taking full ownership of end-to-end operational workflows.

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Inference & Engineering

Lost in the Middle

The phenomenon where LLMs process information in the middle of long contexts worse than at the beginning or end. Documented by Liu et al. (2024) and confirmed by Chroma Research (2025). Requires strategic placement of critical information in context.

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Agentic Infrastructure

LLM-as-a-Judge Evaluations

LLM-as-a-Judge Evaluations is a AI infrastructure concept in modern AI systems that provides foundational capabilities for AI system deployment and operation. It plays a key role in enterprise AI deployments where reliability and scalability are critical for production workloads.

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Agentic UX

llms.txt

A standard initiated by Jeremy Howard – a structured text file in a website's root directory that provides LLMs with optimized information about the website.

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Agentic Infrastructure

Local AI Inference

Running AI model predictions directly on a user's device rather than sending data to cloud servers, providing privacy, lower latency, and no API costs.

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Reasoning & Reliability

Long-Context Model

An AI language model capable of processing very large input sequences (hundreds of thousands to millions of tokens), enabling analysis of entire codebases or long documents without losing context.

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Agentic Infrastructure

Long-Term Memory Layers

Long-Term Memory Layers is a AI infrastructure concept in modern AI systems that provides foundational capabilities for AI system deployment and operation. It plays a key role in enterprise AI deployments where reliability and scalability are critical for production workloads.

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