lAItest / AI field guide · edition 2026-09-26
Know the language. Build with clarity.
Plain-English definitions for the concepts, patterns, protocols, and tools behind modern AI work.
414 defined terms · 16 topic areas · 50 essentials
Agents & architecture terms
23 matching terms.
- Agent Concept · Agents & architecture
A system that selects actions, observes their results, and continues toward an objective.
- Bounded autonomy Concept · Agents & architecture
Independent action within explicit permissions, budgets, scope, and stop conditions.
- Harness Concept · Agents & architecture
The surrounding execution system: model calls, tools, state, context, permissions, and stopping rules.
- Harness engineering Concept · Agents & architecture
Improving the environment and controls around an agent, rather than only its prompt or model.
- Agent framework Concept · Agents & architecture
A developer library offering abstractions for agents, tools, state, or workflows.
- Agent runtime Concept · Agents & architecture
The execution machinery that processes model calls, tool requests, state transitions, and recovery.
- Agentic AI Concept · Agents & architecture
A broad label for systems where models select or sequence actions toward an objective; the label does not specify autonomy, tools, or controls.
- Agentic engineering Concept · Agents & architecture
Software engineering with agents while retaining explicit requirements, verification, and control.
- Agentic workflow Concept · Agents & architecture
A workflow containing model-directed decisions; it can still have deterministic stages and approvals.
- AgentOps Concept · Agents & architecture
Operational practices focused on agent trajectories, actions, permissions, reliability, and cost.
- AI engineering Concept · Agents & architecture
Engineering applications around models: interfaces, data, tools, evaluations, deployment, and operations.
- AI gateway Concept · Agents & architecture
A service that centralizes model routing, policy, observability, and provider access for AI applications.
- AI-native Concept · Agents & architecture
A broad product label implying AI shapes the core workflow rather than being an incidental feature.
- Control plane Concept · Agents & architecture
The part of a system that configures, schedules, authorizes, and coordinates execution.
- Copilot Concept · Agents & architecture
An assistant-oriented interaction pattern in which a human remains actively involved in the work.
- Data plane Concept · Agents & architecture
The part of a system that carries out requests and processes operational data.
- Deep agent Concept · Agents & architecture
An implementation-dependent label often associated with planning, filesystem access, subagents, and context management.
- Deterministic workflow Concept · Agents & architecture
Application code determines the permitted execution paths, even when individual steps use models.
- LLMOps Concept · Agents & architecture
Operational practices for deploying, evaluating, monitoring, and changing language-model applications.
- Long-horizon agent Concept · Agents & architecture
An agent performing many dependent steps, potentially across sessions or context resets.
- Orchestration Concept · Agents & architecture
Coordinating tasks, agents, tools, dependencies, and shared execution state.
- Scaffold Concept · Agents & architecture
Supporting code and structure around model execution; its boundary overlaps with harness in some usage.
- Vibe coding Concept · Agents & architecture
An informal style of building through conversational AI direction, often without closely inspecting generated implementation.
Browse by topic
- Agents & architecture 23 — Who decides, what executes, and where control lives.
- Goals, specs & plans 23 — Describe the outcome before delegating the implementation.
- Loops, critics & adversaries 33 — Understand how work is challenged, repaired, and stopped.
- Multi-agent coordination 21 — Delegation, ownership, context boundaries, and aggregation.
- Context & memory 29 — What the model sees now, and what persists for later.
- Tools & output contracts 20 — Model proposals become validated, authorized operations.
- Protocols & interoperability 19 — Name the boundary: tools, agents, editors, or interfaces.
- Coding-agent internals 25 — Instructions, skills, hooks, tools, and durable artifacts.
- Retrieval & knowledge 31 — Find evidence, rank it, and preserve source boundaries.
- Evals & observability 29 — Measure outcomes and inspect the execution path.
- Inference & performance 28 — Latency, throughput, compute, and memory are different constraints.
- Models, reasoning & training 36 — Separate weight changes from context and inference-time work.
- Security & reliability 27 — Make privileges explicit and side effects recoverable.
- Generative UI & voice 18 — The interaction layer has its own contracts and timing.
- Generative & physical AI 8 — Broader model families beyond text-based assistants.
- Tools & ecosystem 44 — Recognize the role before choosing the dependency.