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AI Knowledge Base 2026

AI Glossary 2026

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

Agentic Infrastructure

Agent Runtime Architecture

Agent runtime architecture refers to the technical execution environment in which AI agents process tasks, invoke tools, and manage state. It is the layer between the language model and external systems — defining how an agent plans steps, handles errors, coordinates parallel subtasks, and maintains context across sessions. Key components include the orchestrator (which controls execution flow), the tool registry (what capabilities the agent can call), session state (short-term working memory), and persistent workspaces (for long-running tasks that survive interruptions). Modern runtimes such as OpenAI Agents SDK v0.14, LangGraph, and Anthropic's native agent infrastructure differ primarily in how they handle state persistence, parallelism, and fault tolerance. Understanding runtime architecture is critical when agents need to do more than answer one-shot queries — especially for workflows that span hours, involve dozens of tool calls, and must recover gracefully from failures.

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Agentic Infrastructure

Agent-Accessible APIs

Agent-Accessible APIs are interfaces intentionally designed for autonomous AI agents, not just human developers. The foundation is machine readability: explicit OpenAPI or JSON Schema contracts, predictable parameters, stable field names, and consistent error semantics. Agents also need deterministic and idempotent operations so retries do not create duplicate orders, bookings, or state changes. Production-grade agent APIs pair this with scoped authentication, auditable actions, rate limits, and policy guardrails. In modern stacks, these APIs are exposed as tools—for example through the Model Context Protocol (MCP)—so models can discover capabilities, invoke functions, and return structured outputs reliably. Without this quality bar, agents fall back to brittle UI scraping and ad-hoc parsing, which increases failure rates and security risk. Agent-Accessible APIs are therefore not a nice-to-have; they are core infrastructure for turning AI prototypes into dependable, governable business workflows.

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Economics & Scale

Agentic Compute

Agentic Compute describes the full execution load created when AI agents do more than generate a single answer and instead carry out multi-step work on their own. That load includes model calls, tool calling, browser or API access, code execution, memory reads and writes, retries, and long-running sessions. The term matters because cost and operational risk behave differently for agents than for standard chat interactions. In a normal chat workflow, usage scales mostly with prompt and completion tokens. In agentic compute, it also scales with step count, concurrency, tool usage, loops, tracing, and safety controls. A coding agent that reads files, runs tests, checks logs, and iterates through fixes can consume far more resources than a one-shot model response. For architecture and pricing, that means teams cannot look at token prices alone. They need workflow budgets, runtime limits, concurrency caps, observability, stop conditions, and human approval gates. Agentic Compute is therefore best understood as an operating model for autonomous AI systems, not just as a model-performance metric.

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Agentic Business

AI Coding Agents

AI Coding Agents are autonomous or semi-autonomous AI systems that perform software development tasks independently or in collaboration with human developers. Unlike traditional code-completion tools like IntelliSense, these agents operate at a higher level of abstraction: they analyze requirements, plan implementation steps, write code, execute tests, and iterate based on feedback. Examples include Claude Code by Anthropic, Cursor with its integrated AI assistant, and OpenAI's Codex. These systems combine large language models with tool calling, file access, terminal commands, and sometimes browser automation to tackle complex development tasks. The key difference from passive assistance systems lies in the agent architecture: they run their own loop (Agent Loop) where they plan, act, observe results, and adapt their strategy—similar to a human developer in miniature.

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Agentic Business

Agent Orchestration

Agent orchestration refers to the coordination of multiple AI agents by a central orchestrator agent or orchestration system to solve complex tasks that individual agents cannot efficiently handle alone. The orchestration layer determines which agents are called when, how results are merged, and how errors are managed. A typical orchestration pattern works as follows: an orchestrator receives a complex task, decomposes it into subtasks, distributes these to specialized sub-agents (e.g., research agent, writing agent, SEO agent), collects results, resolves conflicts, and delivers the final output. The orchestrator itself is often an LLM that monitors progress and dynamically decides next steps. Orchestration strategies include: sequential orchestration (agents work one after another), parallel orchestration (agents work simultaneously on different subtasks), hierarchical orchestration (nested agent teams), and dynamic orchestration (the orchestrator decides at runtime which agents are needed). Key challenges include: error propagation (a failed sub-agent can block the entire system), state management (the orchestrator must maintain context of all running agents), cost control (multiple agents multiply token costs), and observability (tracing what each agent did and why). Frameworks supporting agent orchestration include LangGraph, CrewAI, AutoGen, OpenAI Swarm, and proprietary systems. The choice of framework has significant implications for flexibility, debugging capabilities, and production reliability.

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Agentic Business

Agent Reliability

Agent reliability refers to the degree to which an AI agent consistently and correctly completes desired tasks without unexpected failures, runaway behavior, or deviations from intended operation. It is one of the most critical requirements for deploying AI agents in production environments. Factors affecting reliability: determinism (does the agent run consistently given the same input?), error handling (does the agent gracefully recognize and manage failures?), edge case robustness (how does the agent respond to unexpected inputs?), resource constraints (does the agent respect cost and token budgets?), and hallucination rate (how often does the agent fabricate incorrect information?). Metrics for agent reliability include: task completion rate (percentage of successful runs), mean time between failures (MTBF), error recovery rate (how often does the agent self-recover from error states?), and output consistency score (alignment between expected and actual outputs). Strategies to improve reliability: spec-driven scaffolding (clear execution frameworks), phase budgets (prevent infinite loops), robust error handling with fallbacks, regular evaluation with regression tests, and monitoring systems that detect anomalies. As agentic systems become more capable and autonomous, reliability engineering becomes increasingly important — an unreliable agent given powerful tools is a liability, not an asset. The field of "agent reliability engineering" is emerging as a distinct discipline.

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Agentic Business

Agentic Coding

Agentic coding is an emerging paradigm in software development where AI agents autonomously write, test, debug, and refactor code with minimal human intervention. Unlike traditional AI code completion tools like GitHub Copilot that suggest individual lines or blocks, agentic coding systems like Apple's Xcode 26.3 integration with Claude Agent and OpenAI Codex can execute multi-step development workflows: interpreting high-level requirements, generating implementation plans, writing code across multiple files, running test suites, diagnosing failures, and iterating until the code passes. Agentic coding represents the convergence of large language models (LLMs), tool use capabilities, and development environment integration. Leading implementations include Anthropic's Claude Code, OpenAI's Codex agent, Cursor's composer mode, and Apple's Xcode agentic features. The key differentiator from conventional AI-assisted coding is autonomy — agentic systems can operate in background loops, making decisions about architecture, error handling, and optimization without requiring approval at each step. For enterprises, agentic coding promises 3-10x productivity gains on routine development tasks while raising important questions about code review, security auditing, and architectural oversight.

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Agentic Business

AI Computer Use

AI computer use refers to the ability of AI agents to directly operate a computer — moving the mouse, clicking, typing text, reading screen content, and accessing applications — exactly as a human user would. This capability was introduced in 2024 by Anthropic with Claude as the first widely available implementation. Unlike traditional browser automation (which relies on structured APIs, CSS selectors, and predefined scripts), a computer use agent works at the pixel level: it sees a screenshot of the screen, decides where to click or what to type, executes the action, and observes the result. This approach is universal — it works with any application and any website without specialized engineering. Practical capabilities include: navigating any website without API access, interacting with desktop applications, filling out forms, extracting data from visual interfaces, and executing multi-step workflows that lack programmatic interfaces. Computer use also has known limitations: it is slower than direct API calls (since each step requires a screenshot), more prone to errors when unexpected UI changes occur, and more expensive in token consumption since screenshots are included as input. Nevertheless, it remains the only practical option for many automation tasks that offer no API. Security is a critical consideration: computer use agents have access to whatever is visible on screen and can interact with any UI element, requiring careful sandboxing and permission management to prevent unintended actions.

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Agentic Infrastructure

AI Inference

AI inference is the process by which a trained machine learning model processes new input data to generate predictions, text, images, or other outputs. Unlike training — where a model learns from datasets and adjusts parameters — inference uses a fully trained model to perform specific tasks in real time or batch mode. The economic distinction is fundamental: training a frontier LLM costs $1M–$100M+ as a one-time expense. Inference, by contrast, occurs with every user request — thousands to billions of times daily. As millions of users interact with AI services, cumulative inference costs far exceed training costs over the deployed model's lifetime. Key metrics include Time-to-First-Token (TTFT) measuring latency before the first response token, and Tokens per Second (TPS) measuring throughput. Infrastructure choices divide between batch inference — bulk processing with latency tolerance — and real-time inference requiring sub-second response for interactive applications like chatbots and coding assistants. Optimization techniques span multiple layers: quantization (FP32 → INT8/FP4 for 2–4× speedup), model pruning, speculative decoding, and KV-cache optimization. Specialized inference chips — NVIDIA H100/B200, Google TPUs, Groq LPUs — provide orders-of-magnitude improvements in throughput and energy efficiency. Hardware advances (Hopper → Blackwell → Vera Rubin) drive 2–4× cost reductions per token generation, making previously uneconomical use cases viable.

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