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
Multi-agent coordination terms
21 matching terms.
- Handoff Pattern · Multi-agent coordination
Transfer responsibility or conversational control to another agent.
- Subagent Concept · Multi-agent coordination
A delegated agent with its own task scope and often separate context or tool access.
- Agent team Concept · Multi-agent coordination
A collection of agents with assigned roles and a defined way to coordinate.
- Agent-as-a-tool Pattern · Multi-agent coordination
A parent invokes a subordinate agent and receives its result while retaining overall control.
- Blackboard architecture Pattern · Multi-agent coordination
Coordinate independent contributors through a shared workspace of facts, tasks, and partial solutions.
- Context isolation Concept · Multi-agent coordination
Keep task-specific history and working information out of unrelated agents' contexts.
- Correlated failures Concept · Multi-agent coordination
Multiple agents fail for shared reasons, such as the same missing evidence or model biases.
- Ensemble Concept · Multi-agent coordination
Combine predictions or outputs from multiple runs or models; it need not involve agent collaboration.
- Executor Concept · Multi-agent coordination
A role that carries out assigned actions or implementation work.
- Fan-out / fan-in Pattern · Multi-agent coordination
Run independent branches concurrently, then combine their results.
- Hierarchical agents Pattern · Multi-agent coordination
Organize agents in multiple levels of planning, supervision, and execution.
- Human-in-the-loop Pattern · Multi-agent coordination
A human participates at a specified decision, review, or approval stage.
- Human-on-the-loop Pattern · Multi-agent coordination
A human supervises ongoing execution and can intervene without approving every step.
- Multi-agent debate Pattern · Multi-agent coordination
Have multiple agents challenge or compare candidate answers before selecting or synthesizing an outcome.
- Multi-agent systems Concept · Multi-agent coordination
A system in which multiple model-driven components coordinate to complete a shared or decomposed objective.
- Orchestrator Concept · Multi-agent coordination
A coordinator that assigns work, manages dependencies, and synthesizes results.
- Planner Concept · Multi-agent coordination
A role responsible for structuring the approach and decomposing tasks.
- Router Concept · Multi-agent coordination
Select an agent, model, tool, or workflow based on the request and current state.
- Shared state Concept · Multi-agent coordination
Data that multiple participants can read or update during coordination.
- Supervisor Concept · Multi-agent coordination
An oversight role that routes work, checks progress, and may intervene or escalate.
- Swarm Concept · Multi-agent coordination
An informal label for a collection of cooperating agents; the coordination rules still need specification.
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.